# A tibble: 6 × 81
    Bin  Year ADM1_PCODE ADM2_PCODE State    Municipality      Bin_attacks2 Bin_inc Bin_chal
  <dbl> <dbl> <chr>           <dbl> <chr>    <chr>                    <dbl>   <dbl>    <dbl>
1     1  2012 MX04               NA Campeche Dzitbalche                   0       0        0
2     1  2012 MX04               NA Campeche Seybaplaya                   0       0        0
3     1  2012 MX07               NA Chiapas  Capitán Luis Áng…            0       0        0
4     1  2012 MX07               NA Chiapas  El Parral                    0       0        0
5     1  2012 MX07               NA Chiapas  Emiliano Zapata              0       0        0
6     1  2012 MX07               NA Chiapas  Honduras de la S…            0       0        0
# ℹ 72 more variables: MajorHighway <dbl>, MajorPort <dbl>, Airports <dbl>, Railline <dbl>,
#   Oilline <dbl>, Intlborder <dbl>, Shoreline <dbl>, Poppies <dbl>, MajCity <dbl>,
#   MayorParty <chr>, Incumbent <dbl>, population <dbl>, hom_rate <dbl>,
#   Aguacate_sembrada_tonelada <dbl>, Aguacate_valor_miles_de_pesos <dbl>,
#   Limon_sembrada <dbl>, Limon_valor_prod <dbl>, id_number <dbl>, total_alt <dbl>,
#   Governor_party <chr>, Pres_party <chr>, state_misalign <dbl>, Pres_misalign <dbl>,
#   Upper_misalign <dbl>, inflorgcrime <dbl>, inflfinanciamiento <dbl>, …
# A tibble: 6 × 81
    Bin  Year ADM1_PCODE ADM2_PCODE State  Municipality        Bin_attacks2 Bin_inc Bin_chal
  <dbl> <dbl> <chr>           <dbl> <chr>  <chr>                      <dbl>   <dbl>    <dbl>
1     2  2015 MX20            20397 Oaxaca Heroica Ciudad de …            0       0        0
2     2  2015 MX20            20057 Oaxaca Matias Romero Aven…            0       0        0
3     2  2015 MX20            20067 Oaxaca Oaxaca de Juarez               0       0        0
4     2  2015 MX20            20073 Oaxaca Putla Villa de Gue…            0       0        0
5     2  2015 MX20            20079 Oaxaca Salina Cruz                    0       0        0
6     2  2015 MX20            20134 Oaxaca San Felipe Jalapa              0       0        0
# ℹ 72 more variables: MajorHighway <dbl>, MajorPort <dbl>, Airports <dbl>, Railline <dbl>,
#   Oilline <dbl>, Intlborder <dbl>, Shoreline <dbl>, Poppies <dbl>, MajCity <dbl>,
#   MayorParty <chr>, Incumbent <dbl>, population <dbl>, hom_rate <dbl>,
#   Aguacate_sembrada_tonelada <dbl>, Aguacate_valor_miles_de_pesos <dbl>,
#   Limon_sembrada <dbl>, Limon_valor_prod <dbl>, id_number <dbl>, total_alt <dbl>,
#   Governor_party <chr>, Pres_party <chr>, state_misalign <dbl>, Pres_misalign <dbl>,
#   Upper_misalign <dbl>, inflorgcrime <dbl>, inflfinanciamiento <dbl>, …

Call:
   felm(formula = Bin_inc ~ stag_ind_bin + Pres_misalign + population +      MajorHighway + MajorPort + Airports + Railline + Oilline +      Intlborder + Shoreline + pri_mayor + state_misalign + Bin:State |      Bin + State | 0 | State, data = df_alt2) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.27148 -0.02661 -0.00639  0.01388  1.74253 

Coefficients:
                           Estimate Cluster s.e. t value Pr(>|t|)    
stag_ind_bin              1.597e-01    1.785e-01   0.895   0.3942    
Pres_misalign            -1.073e-02    3.298e-02  -0.326   0.7522    
population               -2.870e-07    2.269e-07  -1.265   0.2376    
MajorHighway              1.437e-02    9.646e-03   1.490   0.1704    
MajorPort                       NaN    0.000e+00     NaN      NaN    
Airports                  6.793e-03    7.985e-03   0.851   0.4170    
Railline                 -5.880e-03    7.395e-03  -0.795   0.4470    
Oilline                   4.638e-03    1.074e-02   0.432   0.6761    
Intlborder                7.179e-02    6.982e-02   1.028   0.3307    
Shoreline                -2.534e-02    8.857e-03  -2.861   0.0188 *  
pri_mayor                -8.569e-03    7.078e-03  -1.211   0.2569    
state_misalign            5.413e-03    1.385e-02   0.391   0.7050    
Bin:StateBaja California        NaN    0.000e+00     NaN      NaN    
Bin:StateChiapas         -7.096e-04    5.610e-03  -0.126   0.9021    
Bin:StateChihuahua       -9.793e-04    1.590e-03  -0.616   0.5533    
Bin:StateDurango         -2.158e-02    2.357e-02  -0.915   0.3839    
Bin:StateNayarit          6.004e-02    7.016e-02   0.856   0.4143    
Bin:StateOaxaca          -7.402e-03    3.029e-03  -2.443   0.0372 *  
Bin:StatePuebla                 NaN    0.000e+00     NaN      NaN    
Bin:StateSinaloa         -5.897e-03    2.699e-03  -2.184   0.0568 .  
Bin:StateTamaulipas       6.358e-02    5.079e-03  12.517 5.37e-07 ***
Bin:StateZacatecas       -1.542e-03    1.219e-03  -1.265   0.2376    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1616 on 170 degrees of freedom
  (12 observations deleted due to missingness)
Multiple R-squared(full model): 0.1041   Adjusted R-squared: -0.05932 
Multiple R-squared(proj model): 0.03504   Adjusted R-squared: -0.1409 
F-statistic(full model, *iid*):0.6369 on 31 and 170 DF, p-value: 0.9307 
F-statistic(proj model):  3.32 on 22 and 9 DF, p-value: 0.03355 



Call:
   felm(formula = Bin_inc ~ stag_ind_bin + Pres_misalign + population +      MajorHighway + MajorPort + Airports + Railline + Oilline +      Intlborder + Shoreline + pri_mayor + state_misalign + Bin:State |      Bin + State | 0 | State, data = df_alt3) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.15118 -0.01401 -0.00568  0.00101  0.99976 

Coefficients:
                           Estimate Cluster s.e. t value Pr(>|t|)    
stag_ind_bin              7.723e-03    5.738e-03   1.346   0.1997    
Pres_misalign             4.242e-03    7.780e-03   0.545   0.5942    
population                8.549e-08    4.922e-08   1.737   0.1044    
MajorHighway              2.710e-03    1.329e-03   2.039   0.0608 .  
MajorPort                -1.665e-02    1.374e-02  -1.212   0.2457    
Airports                 -1.826e-02    6.751e-03  -2.705   0.0171 *  
Railline                  1.275e-02    6.058e-03   2.104   0.0539 .  
Oilline                  -6.724e-03    6.823e-03  -0.985   0.3411    
Intlborder               -5.756e-03    2.837e-03  -2.029   0.0619 .  
Shoreline                 5.007e-03    5.386e-03   0.930   0.3683    
pri_mayor                -6.806e-03    7.457e-03  -0.913   0.3769    
state_misalign           -2.135e-03    2.040e-03  -1.046   0.3131    
Bin:StateAguascalientes   7.262e-03    4.375e-03   1.660   0.1192    
Bin:StateBaja California  6.353e-03    3.516e-03   1.807   0.0923 .  
Bin:StateChiapas          1.084e-02    4.096e-03   2.646   0.0192 *  
Bin:StateChihuahua        8.748e-03    4.941e-03   1.771   0.0984 .  
Bin:StateDurango         -2.517e-02    3.073e-03  -8.190 1.04e-06 ***
Bin:StateNayarit          7.431e-03    2.575e-03   2.886   0.0120 *  
Bin:StateOaxaca          -1.484e-03    3.857e-03  -0.385   0.7062    
Bin:StatePuebla           6.465e-03    3.761e-03   1.719   0.1077    
Bin:StateQuintana Roo           NaN    0.000e+00     NaN      NaN    
Bin:StateSinaloa          3.342e-03    1.897e-03   1.762   0.0999 .  
Bin:StateTamaulipas       6.481e-03    3.403e-03   1.904   0.0776 .  
Bin:StateTlaxcala         9.828e-03    3.595e-03   2.734   0.0161 *  
Bin:StateVeracruz        -6.184e-05    3.694e-03  -0.017   0.9869    
Bin:StateYucatan          6.980e-03    4.312e-03   1.619   0.1278    
Bin:StateZacatecas        6.607e-03    4.401e-03   1.501   0.1555    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.08827 on 1987 degrees of freedom
  (194 observations deleted due to missingness)
Multiple R-squared(full model): 0.02468   Adjusted R-squared: 0.003573 
Multiple R-squared(proj model): 0.01706   Adjusted R-squared: -0.004214 
F-statistic(full model, *iid*):1.169 on 43 and 1987 DF, p-value: 0.2106 
F-statistic(proj model): 26.15 on 27 and 14 DF, p-value: 5.314e-08 



Call:
   felm(formula = Bin_inc ~ stag_ind_bin + Pres_misalign + population +      MajorHighway + MajorPort + Airports + Railline + Oilline +      Intlborder + Shoreline + pri_mayor + state_misalign + Bin:State |      Bin + State | 0 | State, data = panel) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.04786 -0.01663 -0.00817 -0.00078  1.97214 

Coefficients:
                               Estimate Cluster s.e. t value Pr(>|t|)    
stag_ind_bin                 -7.488e-03    4.480e-03  -1.672 0.104998    
Pres_misalign                -1.303e-03    5.846e-03  -0.223 0.825062    
population                    7.867e-09    1.220e-08   0.645 0.523848    
MajorHighway                  8.699e-05    3.167e-03   0.027 0.978272    
MajorPort                     7.943e-04    1.502e-02   0.053 0.958170    
Airports                      5.258e-03    4.531e-03   1.160 0.255053    
Railline                     -4.126e-03    1.417e-02  -0.291 0.772930    
Oilline                       1.490e-03    1.458e-02   0.102 0.919301    
Intlborder                    3.962e-03    9.867e-03   0.402 0.690832    
Shoreline                     3.311e-03    3.315e-03   0.999 0.325888    
pri_mayor                    -3.400e-04    2.468e-03  -0.138 0.891330    
state_misalign                5.003e-03    3.815e-03   1.311 0.199752    
Bin:StateAguascalientes       4.777e-04    7.135e-04   0.670 0.508257    
Bin:StateBaja California            NaN    0.000e+00     NaN      NaN    
Bin:StateBaja California Sur -7.285e-05    2.350e-04  -0.310 0.758675    
Bin:StateCampeche             6.453e-04    4.372e-04   1.476 0.150364    
Bin:StateChiapas              2.715e-03    6.787e-04   4.000 0.000382 ***
Bin:StateChihuahua           -6.624e-03    5.773e-04 -11.475 1.69e-12 ***
Bin:StateCoahuila             2.125e-04    5.374e-04   0.395 0.695294    
Bin:StateColima               1.101e-04    3.385e-04   0.325 0.747300    
Bin:StateDurango             -1.680e-02    1.961e-03  -8.571 1.46e-09 ***
Bin:StateGuanajuato           3.154e-03    3.656e-04   8.627 1.27e-09 ***
Bin:StateGuerrero             2.805e-03    7.015e-04   3.998 0.000384 ***
Bin:StateHidalgo             -1.899e-04    1.421e-03  -0.134 0.894556    
Bin:StateJalisco              3.004e-03    4.512e-04   6.656 2.26e-07 ***
Bin:StateMexico               1.298e-03    6.319e-04   2.055 0.048715 *  
Bin:StateMichoacan            1.696e-03    4.209e-04   4.028 0.000353 ***
Bin:StateMorelos              6.823e-04    5.971e-04   1.143 0.262193    
Bin:StateNayarit             -2.277e-03    1.470e-03  -1.549 0.131757    
Bin:StateNuevo Leon           3.559e-04    3.033e-04   1.173 0.249873    
Bin:StateOaxaca              -3.956e-03    3.742e-04 -10.573 1.23e-11 ***
Bin:StatePuebla               4.467e-04    6.778e-04   0.659 0.514914    
Bin:StateQueretaro           -2.610e-04    8.129e-04  -0.321 0.750400    
Bin:StateQuintana Roo         1.038e-03    1.151e-03   0.902 0.374197    
Bin:StateSan Luis Potosi     -5.145e-03    4.401e-04 -11.689 1.07e-12 ***
Bin:StateSinaloa              1.073e-04    3.040e-04   0.353 0.726617    
Bin:StateSonora               9.722e-04    8.707e-04   1.116 0.273077    
Bin:StateTabasco             -5.393e-03    3.448e-04 -15.641 5.74e-16 ***
Bin:StateTamaulipas           6.196e-03    9.253e-04   6.697 2.02e-07 ***
Bin:StateTlaxcala            -2.237e-03    1.398e-03  -1.600 0.119988    
Bin:StateVeracruz            -4.776e-03    1.385e-03  -3.448 0.001696 ** 
Bin:StateYucatan              4.300e-04    5.834e-04   0.737 0.466774    
Bin:StateZacatecas            1.322e-03    1.012e-03   1.306 0.201497    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.0983 on 7312 degrees of freedom
  (419 observations deleted due to missingness)
Multiple R-squared(full model): 0.009993   Adjusted R-squared: -0.000162 
Multiple R-squared(proj model): 0.002248   Adjusted R-squared: -0.007986 
F-statistic(full model, *iid*):0.984 on 75 and 7312 DF, p-value: 0.5176 
F-statistic(proj model): 0.5461 on 43 and 30 DF, p-value: 0.966 



Call:
   felm(formula = Bin_chal ~ stag_ind_bin + Pres_misalign + population +      MajorHighway + MajorPort + Airports + Railline + Oilline +      Intlborder + Shoreline + pri_mayor + state_misalign + Bin:State |      Bin + State | 0 | State, data = df_alt2) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.48086 -0.13905 -0.01626  0.02945  1.76906 

Coefficients:
                           Estimate Cluster s.e.  t value Pr(>|t|)    
stag_ind_bin              4.211e-01    1.258e-01    3.348  0.00855 ** 
Pres_misalign            -1.461e-02    4.950e-02   -0.295  0.77451    
population                1.046e-06    2.670e-07    3.918  0.00352 ** 
MajorHighway             -2.260e-02    3.876e-02   -0.583  0.57406    
MajorPort                       NaN    0.000e+00      NaN      NaN    
Airports                 -2.111e-02    2.530e-02   -0.834  0.42571    
Railline                  5.595e-02    7.227e-02    0.774  0.45871    
Oilline                   4.438e-02    1.048e-01    0.423  0.68196    
Intlborder                7.681e-02    1.224e-01    0.628  0.54587    
Shoreline                -2.088e-02    3.533e-02   -0.591  0.56904    
pri_mayor                -4.651e-02    4.959e-02   -0.938  0.37278    
state_misalign            1.205e-01    4.260e-02    2.830  0.01974 *  
Bin:StateBaja California        NaN    0.000e+00      NaN      NaN    
Bin:StateChiapas         -2.859e-01    6.952e-03  -41.120 1.48e-11 ***
Bin:StateChihuahua       -2.882e-01    3.288e-03  -87.648 1.66e-14 ***
Bin:StateDurango         -3.793e-01    1.861e-02  -20.377 7.70e-09 ***
Bin:StateNayarit         -1.411e-01    5.157e-02   -2.737  0.02297 *  
Bin:StateOaxaca          -2.250e-01    2.550e-03  -88.236 1.56e-14 ***
Bin:StatePuebla                 NaN    0.000e+00      NaN      NaN    
Bin:StateSinaloa         -3.064e-01    1.345e-02  -22.780 2.87e-09 ***
Bin:StateTamaulipas      -3.857e-01    2.121e-02  -18.179 2.10e-08 ***
Bin:StateZacatecas       -2.944e-01    1.434e-03 -205.244  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.3156 on 170 degrees of freedom
  (12 observations deleted due to missingness)
Multiple R-squared(full model): 0.1835   Adjusted R-squared: 0.03464 
Multiple R-squared(proj model): 0.08856   Adjusted R-squared: -0.07764 
F-statistic(full model, *iid*):1.233 on 31 and 170 DF, p-value: 0.2013 
F-statistic(proj model):  5.78 on 22 and 9 DF, p-value: 0.004988 



Call:
   felm(formula = Bin_chal ~ stag_ind_bin + Pres_misalign + population +      MajorHighway + MajorPort + Airports + Railline + Oilline +      Intlborder + Shoreline + pri_mayor + state_misalign + Bin:State |      Bin + State | 0 | State, data = df_alt3) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.43551 -0.04074 -0.01239  0.00281  1.93394 

Coefficients:
                           Estimate Cluster s.e. t value Pr(>|t|)    
stag_ind_bin              1.317e-02    9.945e-03   1.325  0.20650    
Pres_misalign            -6.752e-04    9.602e-03  -0.070  0.94493    
population                9.219e-08    5.475e-08   1.684  0.11441    
MajorHighway              1.211e-02    1.239e-02   0.978  0.34480    
MajorPort                -7.794e-02    2.139e-02  -3.644  0.00266 ** 
Airports                  7.099e-02    3.037e-02   2.338  0.03477 *  
Railline                 -2.574e-02    1.294e-02  -1.989  0.06658 .  
Oilline                   5.694e-04    1.124e-02   0.051  0.96033    
Intlborder                5.247e-03    2.425e-02   0.216  0.83180    
Shoreline                -1.794e-02    1.837e-02  -0.977  0.34517    
pri_mayor                -6.451e-03    7.402e-03  -0.872  0.39816    
state_misalign           -1.170e-03    1.013e-02  -0.116  0.90963    
Bin:StateAguascalientes  -1.145e-01    2.947e-03 -38.846 1.17e-15 ***
Bin:StateBaja California -1.153e-01    3.776e-03 -30.532 3.27e-14 ***
Bin:StateChiapas         -1.155e-01    2.367e-03 -48.769  < 2e-16 ***
Bin:StateChihuahua       -9.332e-02    4.435e-03 -21.044 5.39e-12 ***
Bin:StateDurango         -1.286e-01    6.992e-03 -18.390 3.34e-11 ***
Bin:StateNayarit         -1.004e-01    3.936e-03 -25.519 3.87e-13 ***
Bin:StateOaxaca          -1.088e-01    2.775e-03 -39.216 1.02e-15 ***
Bin:StatePuebla          -1.025e-01    2.499e-03 -41.010 5.50e-16 ***
Bin:StateQuintana Roo           NaN    0.000e+00     NaN      NaN    
Bin:StateSinaloa         -1.071e-01    2.064e-03 -51.890  < 2e-16 ***
Bin:StateTamaulipas      -1.379e-01    4.990e-03 -27.639 1.29e-13 ***
Bin:StateTlaxcala        -1.085e-01    4.193e-03 -25.883 3.18e-13 ***
Bin:StateVeracruz        -8.942e-02    5.072e-03 -17.631 5.90e-11 ***
Bin:StateYucatan         -1.150e-01    2.585e-03 -44.481  < 2e-16 ***
Bin:StateZacatecas       -1.160e-01    2.460e-03 -47.146  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1608 on 1987 degrees of freedom
  (194 observations deleted due to missingness)
Multiple R-squared(full model): 0.06216   Adjusted R-squared: 0.04187 
Multiple R-squared(proj model): 0.0263   Adjusted R-squared: 0.005233 
F-statistic(full model, *iid*):3.063 on 43 and 1987 DF, p-value: 1.683e-10 
F-statistic(proj model): 177.8 on 27 and 14 DF, p-value: 1.067e-13 



Call:
   felm(formula = Bin_chal ~ stag_ind_bin + Pres_misalign + population +      MajorHighway + MajorPort + Airports + Railline + Oilline +      Intlborder + Shoreline + pri_mayor + state_misalign + Bin:State |      Bin + State | 0 | State, data = panel) 

Residuals:
    Min      1Q  Median      3Q     Max 
-0.2890 -0.0639 -0.0215  0.0013  6.7877 

Coefficients:
                               Estimate Cluster s.e. t value Pr(>|t|)    
stag_ind_bin                 -1.136e-02    8.308e-03  -1.367  0.18167    
Pres_misalign                 1.356e-02    9.308e-03   1.457  0.15562    
population                    9.154e-08    3.978e-08   2.301  0.02851 *  
MajorHighway                  1.192e-02    4.178e-03   2.854  0.00776 ** 
MajorPort                    -2.583e-02    5.954e-02  -0.434  0.66756    
Airports                      1.978e-02    8.176e-03   2.419  0.02182 *  
Railline                     -1.863e-02    2.305e-02  -0.809  0.42515    
Oilline                       1.260e-02    2.266e-02   0.556  0.58222    
Intlborder                    3.537e-03    2.025e-02   0.175  0.86248    
Shoreline                     1.055e-02    1.549e-02   0.681  0.50102    
pri_mayor                    -1.965e-02    7.003e-03  -2.806  0.00873 ** 
state_misalign                5.025e-03    8.363e-03   0.601  0.55243    
Bin:StateAguascalientes      -5.443e-02    1.956e-03 -27.832  < 2e-16 ***
Bin:StateBaja California            NaN    0.000e+00     NaN      NaN    
Bin:StateBaja California Sur -5.853e-02    6.165e-04 -94.936  < 2e-16 ***
Bin:StateCampeche            -4.724e-03    9.448e-04  -5.000 2.33e-05 ***
Bin:StateChiapas             -4.586e-02    2.030e-03 -22.592  < 2e-16 ***
Bin:StateChihuahua           -5.669e-02    1.104e-03 -51.346  < 2e-16 ***
Bin:StateCoahuila            -5.502e-02    1.545e-03 -35.607  < 2e-16 ***
Bin:StateColima              -4.264e-02    1.052e-03 -40.523  < 2e-16 ***
Bin:StateDurango             -5.509e-02    4.685e-03 -11.759 9.24e-13 ***
Bin:StateGuanajuato          -1.725e-02    1.103e-03 -15.637 5.77e-16 ***
Bin:StateGuerrero            -1.233e-02    2.162e-03  -5.701 3.22e-06 ***
Bin:StateHidalgo             -4.434e-02    2.864e-03 -15.483 7.53e-16 ***
Bin:StateJalisco             -5.867e-02    1.046e-03 -56.096  < 2e-16 ***
Bin:StateMexico              -4.187e-02    1.535e-03 -27.277  < 2e-16 ***
Bin:StateMichoacan           -3.825e-02    1.293e-03 -29.573  < 2e-16 ***
Bin:StateMorelos              5.566e-03    1.703e-03   3.267  0.00272 ** 
Bin:StateNayarit             -5.505e-02    2.897e-03 -19.003  < 2e-16 ***
Bin:StateNuevo Leon          -4.670e-02    8.178e-04 -57.107  < 2e-16 ***
Bin:StateOaxaca              -3.881e-02    1.170e-03 -33.171  < 2e-16 ***
Bin:StatePuebla              -4.582e-02    1.557e-03 -29.434  < 2e-16 ***
Bin:StateQueretaro           -5.197e-02    1.369e-03 -37.956  < 2e-16 ***
Bin:StateQuintana Roo        -3.603e-03    2.270e-03  -1.587  0.12294    
Bin:StateSan Luis Potosi     -4.129e-02    1.381e-03 -29.904  < 2e-16 ***
Bin:StateSinaloa             -5.398e-02    7.938e-04 -68.007  < 2e-16 ***
Bin:StateSonora              -4.480e-02    1.981e-03 -22.610  < 2e-16 ***
Bin:StateTabasco             -3.157e-02    1.112e-03 -28.386  < 2e-16 ***
Bin:StateTamaulipas          -6.191e-02    1.786e-03 -34.669  < 2e-16 ***
Bin:StateTlaxcala            -5.562e-02    3.448e-03 -16.131 2.50e-16 ***
Bin:StateVeracruz            -3.289e-02    2.884e-03 -11.406 1.96e-12 ***
Bin:StateYucatan             -5.541e-02    1.612e-03 -34.371  < 2e-16 ***
Bin:StateZacatecas           -5.845e-02    2.396e-03 -24.394  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2258 on 7312 degrees of freedom
  (419 observations deleted due to missingness)
Multiple R-squared(full model): 0.04849   Adjusted R-squared: 0.03873 
Multiple R-squared(proj model): 0.01158   Adjusted R-squared: 0.001443 
F-statistic(full model, *iid*):4.968 on 75 and 7312 DF, p-value: < 2.2e-16 
F-statistic(proj model): 8.683 on 43 and 30 DF, p-value: 1.146e-08 


[1] 0.03319502
[1] 0.03727424

Call:
   felm(formula = Bin_attacks2 ~ stag_ind_bin + Pres_misalign +      population + MajorHighway + MajorPort + Airports + Railline +      Oilline + Intlborder + Shoreline + pri_mayor + state_misalign +      Bin:State | Bin + State | 0 | State, data = df_alt2) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.50416 -0.14084 -0.02266  0.04257  1.72811 

Coefficients:
                           Estimate Cluster s.e.  t value Pr(>|t|)    
stag_ind_bin              5.809e-01    2.913e-01    1.994   0.0773 .  
Pres_misalign            -2.535e-02    7.639e-02   -0.332   0.7476    
population                7.593e-07    3.775e-07    2.011   0.0751 .  
MajorHighway             -8.232e-03    3.636e-02   -0.226   0.8259    
MajorPort                       NaN    0.000e+00      NaN      NaN    
Airports                 -1.431e-02    2.721e-02   -0.526   0.6115    
Railline                  5.007e-02    6.843e-02    0.732   0.4830    
Oilline                   4.902e-02    9.926e-02    0.494   0.6333    
Intlborder                1.486e-01    1.917e-01    0.775   0.4580    
Shoreline                -4.622e-02    2.926e-02   -1.580   0.1487    
pri_mayor                -5.508e-02    5.108e-02   -1.078   0.3090    
state_misalign            1.259e-01    4.931e-02    2.554   0.0310 *  
Bin:StateBaja California        NaN    0.000e+00      NaN      NaN    
Bin:StateChiapas         -2.866e-01    1.232e-02  -23.259 2.39e-09 ***
Bin:StateChihuahua       -2.892e-01    4.286e-03  -67.473 1.74e-13 ***
Bin:StateDurango         -4.008e-01    3.572e-02  -11.221 1.36e-06 ***
Bin:StateNayarit         -8.110e-02    1.179e-01   -0.688   0.5090    
Bin:StateOaxaca          -2.324e-01    4.768e-03  -48.733 3.23e-12 ***
Bin:StatePuebla                 NaN    0.000e+00      NaN      NaN    
Bin:StateSinaloa         -3.123e-01    1.274e-02  -24.522 1.49e-09 ***
Bin:StateTamaulipas      -3.221e-01    2.295e-02  -14.034 2.01e-07 ***
Bin:StateZacatecas       -2.959e-01    2.028e-03 -145.953  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.342 on 170 degrees of freedom
  (12 observations deleted due to missingness)
Multiple R-squared(full model): 0.2116   Adjusted R-squared: 0.06779 
Multiple R-squared(proj model): 0.08075   Adjusted R-squared: -0.08687 
F-statistic(full model, *iid*):1.471 on 31 and 170 DF, p-value: 0.0643 
F-statistic(proj model): 2.132 on 22 and 9 DF, p-value: 0.1202 



Call:
   felm(formula = Bin_attacks2 ~ stag_ind_bin + Pres_misalign +      population + MajorHighway + MajorPort + Airports + Railline +      Oilline + Intlborder + Shoreline + pri_mayor + state_misalign +      Bin:State | Bin + State | 0 | State, data = df_alt3) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.47041 -0.05287 -0.02184 -0.00047  1.92222 

Coefficients:
                           Estimate Cluster s.e. t value Pr(>|t|)    
stag_ind_bin              2.090e-02    9.306e-03   2.245  0.04141 *  
Pres_misalign             3.567e-03    7.010e-03   0.509  0.61879    
population                1.777e-07    1.004e-07   1.770  0.09854 .  
MajorHighway              1.482e-02    1.207e-02   1.228  0.23967    
MajorPort                -9.459e-02    2.376e-02  -3.981  0.00137 ** 
Airports                  5.272e-02    3.217e-02   1.639  0.12354    
Railline                 -1.299e-02    1.469e-02  -0.884  0.39147    
Oilline                  -6.154e-03    1.628e-02  -0.378  0.71111    
Intlborder               -5.092e-04    2.330e-02  -0.022  0.98288    
Shoreline                -1.294e-02    1.444e-02  -0.896  0.38547    
pri_mayor                -1.326e-02    3.351e-03  -3.957  0.00143 ** 
state_misalign           -3.305e-03    1.044e-02  -0.317  0.75619    
Bin:StateAguascalientes  -1.072e-01    6.480e-03 -16.547 1.38e-10 ***
Bin:StateBaja California -1.089e-01    6.703e-03 -16.253 1.75e-10 ***
Bin:StateChiapas         -1.046e-01    5.864e-03 -17.843 5.02e-11 ***
Bin:StateChihuahua       -8.458e-02    8.702e-03  -9.719 1.33e-07 ***
Bin:StateDurango         -1.538e-01    7.477e-03 -20.565 7.37e-12 ***
Bin:StateNayarit         -9.301e-02    6.061e-03 -15.346 3.76e-10 ***
Bin:StateOaxaca          -1.103e-01    6.129e-03 -17.998 4.47e-11 ***
Bin:StatePuebla          -9.600e-02    5.208e-03 -18.433 3.24e-11 ***
Bin:StateQuintana Roo           NaN    0.000e+00     NaN      NaN    
Bin:StateSinaloa         -1.037e-01    3.452e-03 -30.053 4.07e-14 ***
Bin:StateTamaulipas      -1.315e-01    7.255e-03 -18.118 4.09e-11 ***
Bin:StateTlaxcala        -9.870e-02    7.568e-03 -13.043 3.19e-09 ***
Bin:StateVeracruz        -8.948e-02    8.440e-03 -10.603 4.50e-08 ***
Bin:StateYucatan         -1.080e-01    6.189e-03 -17.451 6.77e-11 ***
Bin:StateZacatecas       -1.094e-01    5.951e-03 -18.383 3.36e-11 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1846 on 1987 degrees of freedom
  (194 observations deleted due to missingness)
Multiple R-squared(full model): 0.05795   Adjusted R-squared: 0.03756 
Multiple R-squared(proj model): 0.02594   Adjusted R-squared: 0.004857 
F-statistic(full model, *iid*):2.843 on 43 and 1987 DF, p-value: 3.548e-09 
F-statistic(proj model): 162.6 on 27 and 14 DF, p-value: 1.987e-13 



Call:
   felm(formula = Bin_attacks2 ~ stag_ind_bin + Pres_misalign +      population + MajorHighway + MajorPort + Airports + Railline +      Oilline + Intlborder + Shoreline + pri_mayor + state_misalign +      Bin:State | Bin + State | 0 | State, data = panel) 

Residuals:
    Min      1Q  Median      3Q     Max 
-0.3287 -0.0741 -0.0299 -0.0033  6.7563 

Coefficients:
                               Estimate Cluster s.e. t value Pr(>|t|)    
stag_ind_bin                 -1.885e-02    1.025e-02  -1.839 0.075882 .  
Pres_misalign                 1.225e-02    1.220e-02   1.004 0.323330    
population                    9.941e-08    4.351e-08   2.285 0.029566 *  
MajorHighway                  1.201e-02    3.850e-03   3.120 0.003979 ** 
MajorPort                    -2.503e-02    5.598e-02  -0.447 0.657927    
Airports                      2.504e-02    9.315e-03   2.688 0.011619 *  
Railline                     -2.276e-02    3.672e-02  -0.620 0.540016    
Oilline                       1.409e-02    3.679e-02   0.383 0.704338    
Intlborder                    7.500e-03    2.880e-02   0.260 0.796355    
Shoreline                     1.386e-02    1.544e-02   0.898 0.376399    
pri_mayor                    -1.999e-02    6.775e-03  -2.950 0.006104 ** 
state_misalign                1.003e-02    1.010e-02   0.993 0.328681    
Bin:StateAguascalientes      -5.395e-02    2.016e-03 -26.762  < 2e-16 ***
Bin:StateBaja California            NaN    0.000e+00     NaN      NaN    
Bin:StateBaja California Sur -5.860e-02    6.105e-04 -95.995  < 2e-16 ***
Bin:StateCampeche            -4.078e-03    9.800e-04  -4.161 0.000245 ***
Bin:StateChiapas             -4.314e-02    2.113e-03 -20.413  < 2e-16 ***
Bin:StateChihuahua           -6.331e-02    1.303e-03 -48.575  < 2e-16 ***
Bin:StateCoahuila            -5.480e-02    1.691e-03 -32.411  < 2e-16 ***
Bin:StateColima              -4.253e-02    1.047e-03 -40.614  < 2e-16 ***
Bin:StateDurango             -7.190e-02    5.670e-03 -12.682 1.39e-13 ***
Bin:StateGuanajuato          -1.410e-02    1.149e-03 -12.271 3.19e-13 ***
Bin:StateGuerrero            -9.522e-03    2.211e-03  -4.307 0.000163 ***
Bin:StateHidalgo             -4.453e-02    3.549e-03 -12.548 1.81e-13 ***
Bin:StateJalisco             -5.566e-02    1.121e-03 -49.647  < 2e-16 ***
Bin:StateMexico              -4.058e-02    1.584e-03 -25.623  < 2e-16 ***
Bin:StateMichoacan           -3.655e-02    1.247e-03 -29.319  < 2e-16 ***
Bin:StateMorelos              6.248e-03    1.678e-03   3.724 0.000811 ***
Bin:StateNayarit             -5.733e-02    3.783e-03 -15.154 1.34e-15 ***
Bin:StateNuevo Leon          -4.634e-02    7.787e-04 -59.515  < 2e-16 ***
Bin:StateOaxaca              -4.277e-02    1.147e-03 -37.283  < 2e-16 ***
Bin:StatePuebla              -4.537e-02    1.514e-03 -29.976  < 2e-16 ***
Bin:StateQueretaro           -5.223e-02    1.745e-03 -29.926  < 2e-16 ***
Bin:StateQuintana Roo        -2.565e-03    2.389e-03  -1.073 0.291617    
Bin:StateSan Luis Potosi     -4.644e-02    1.343e-03 -34.576  < 2e-16 ***
Bin:StateSinaloa             -5.388e-02    8.103e-04 -66.488  < 2e-16 ***
Bin:StateSonora              -4.382e-02    1.903e-03 -23.025  < 2e-16 ***
Bin:StateTabasco             -3.696e-02    1.231e-03 -30.015  < 2e-16 ***
Bin:StateTamaulipas          -5.571e-02    2.293e-03 -24.297  < 2e-16 ***
Bin:StateTlaxcala            -5.786e-02    4.308e-03 -13.431 3.18e-14 ***
Bin:StateVeracruz            -3.767e-02    3.721e-03 -10.123 3.44e-11 ***
Bin:StateYucatan             -5.498e-02    1.660e-03 -33.124  < 2e-16 ***
Bin:StateZacatecas           -5.713e-02    2.570e-03 -22.226  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2514 on 7312 degrees of freedom
  (419 observations deleted due to missingness)
Multiple R-squared(full model): 0.04897   Adjusted R-squared: 0.03922 
Multiple R-squared(proj model): 0.01086   Adjusted R-squared: 0.000714 
F-statistic(full model, *iid*):5.021 on 75 and 7312 DF, p-value: < 2.2e-16 
F-statistic(proj model): 13.14 on 43 and 30 DF, p-value: 5.454e-11 


\begin{table}
\centering
\begin{talltblr}[         %% tabularray outer open
caption={Treatment Effect on Attacks in Criminally-Entrenched Areas and non-Entrenched Areas},
note{}={Significance levels: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.},
]                     %% tabularray outer close
{                     %% tabularray inner open
colspec={Q[]Q[]Q[]Q[]Q[]Q[]Q[]},
column{2-7}={}{halign=c,},
column{1}={}{halign=l,},
hline{4}={1-7}{solid, black, 0.05em},
}                     %% tabularray inner close
\toprule
& All Candidates, CE & Incumbent, CE & Challengers, CE & All Candidates, not CE & Incumbent, not CE & Challengers, not CE \\ \midrule %% TinyTableHeader
Mayoral Reelection & \num{0.581}+ & \num{0.160} & \num{0.421}** & \num{0.021}* & \num{0.008} & \num{0.013} \\
& (\num{0.291}) & (\num{0.179}) & (\num{0.126}) & (\num{0.009}) & (\num{0.006}) & (\num{0.010}) \\
Num.Obs. & \num{202} & \num{202} & \num{202} & \num{2031} & \num{2031} & \num{2031} \\
R2 & \num{0.212} & \num{0.104} & \num{0.184} & \num{0.058} & \num{0.025} & \num{0.062} \\
\bottomrule
\end{talltblr}
\end{table} 
Diff (Challengers): CE - Non-CE = \num{0.408}** \\
SE = \num{0.126}, p = 0.001 \\
\begin{table}

\caption{Difference in ATT Coefficients across Settings by Target Type}
\centering
\begin{tabular}[t]{l}
\toprule
Difference (Entrenched − Non-Entrenched)\\
\midrule
0.560+ (0.291)\\
0.152 (0.179)\\
0.408** (0.126)\\
\bottomrule
\end{tabular}
\end{table}
        muni_id year inegi   ife NOM_EST NOM_MUN CVE_ENT CVE_MUN unique_groups
1 Abala-Yucatan 2009 31001 31001 Yucatan   Abala      31       1             0
2 Abala-Yucatan 2010 31001 31001 Yucatan   Abala      31       1             0
3 Abala-Yucatan 2011 31001 31001 Yucatan   Abala      31       1             0
4 Abala-Yucatan 2012 31001 31001 Yucatan   Abala      31       1             0
5 Abala-Yucatan 2013 31001 31001 Yucatan   Abala      31       1             0
6 Abala-Yucatan 2014 31001 31001 Yucatan   Abala      31       1             0
  dominant splinters cells unaffiliated expansion dominant_expansion
1        0         0     0            0         0                  0
2        0         0     0            0         0                  0
3        0         0     0            0         0                  0
4        0         0     0            0         0                  0
5        0         0     0            0         0                  0
6        0         0     0            0         0                  0
  splinters_expansion cells_expansion unaffiliated_expansion emergence
1                   0               0                      0         0
2                   0               0                      0         0
3                   0               0                      0         0
4                   0               0                      0         0
5                   0               0                      0         0
6                   0               0                      0         0
  dominant_emergence splinters_emergence cells_emergence unaffiliated_emergence
1                  0                   0               0                      0
2                  0                   0               0                      0
3                  0                   0               0                      0
4                  0                   0               0                      0
5                  0                   0               0                      0
6                  0                   0               0                      0
  umbrella affiliated splinter_alt cell_alt unique_groups_nobf dominant_nobf
1        0          0            0        0                  0             0
2        0          0            0        0                  0             0
3        0          0            0        0                  0             0
4        0          0            0        0                  0             0
5        0          0            0        0                  0             0
6        0          0            0        0                  0             0
  splinters_nobf cells_nobf unaffiliated_nobf unique_groups_m10 splinters_m10
1              0          0                 0                 0             0
2              0          0                 0                 0             0
3              0          0                 0                 0             0
4              0          0                 0                 0             0
5              0          0                 0                 0             0
6              0          0                 0                 0             0
  cells_m10 unaffiliated_m10 corroborated splinters_corroborated
1         0                0            0                      0
2         0                0            0                      0
3         0                0            0                      0
4         0                0            0                      0
5         0                0            0                      0
6         0                0            0                      0
  cells_corroborated unaffiliated_corroborated kingpin_tr kingpin_tr_ypre
1                  0                         0          0               0
2                  0                         0          0               0
3                  0                         0          0               0
4                  0                         0          0               0
5                  0                         0          0               0
6                  0                         0          0               0
  kingpin_tr_yof kingpin_tr_on kingpin_tofrom
1              0             0             NA
2              0             0             NA
3              0             0             NA
4              0             0             NA
5              0             0             NA
6              0             0             NA
[1] 0.4909361
[1] 0.1065679
[1] 0.05976055
   [1] 31001  5001 11001 19001 28001 20001  7001 21001 30001  7002 11002 15001
  [13] 31002  7003 18001 12001 21002 12076 14001 14002 20002 13001 21003 30002
  [25] 21004 13002 30003 15002 26001 21005 13003 30004 29022 16001 30005 15003
  [37] 30006  5002 13004 30204 26002 19002  1001 16002 25001 18002 21006 12002
  [49] 14003 24001 21007 21008 21009  8001 13005 21010 12003 31003 30160 26003
  [61] 24002 21011 12004  7113  8002 28002 13006 21012  8003  5003 19004 15004
  [73] 15005 15006 13007 30008 12005 28003  7004 26004 21013 30009 30010 30011
  [85]  9010 16003 14004 17001 15007  7005  7006  7007 15008 14005 30012 18003
  [97] 30014 29001 22001 14006 15009 21014 21015 19005 16004 16005  7008 30015
 [109] 25002 20174 28004 13008 11004 11005 16006 15010 12006 30017 29002 29005
 [121] 19006 16007 32001 32002 16008 30018  8004 24003 21016 19007 14008 12007
 [133] 16009 26005 26006 24004  6001  7009 22003  5004 16010  8005  1002 30019
 [145] 20003 20004 20005 20006 20007 20008 11006 14010 21017 12008 15011 14011
 [157] 14012 21018 26007 13010 15013 15012 15014 30020 12009 29003 13011 17002
 [169] 15015 21080 21019 12010 29004 32003 13013 14013 13012 12011 14014 30021
 [181] 21020 30022 21021 30023 21022 21023 14015 15016 17003 24053 21024 30025
 [193] 17004 15017 20398 14016 21025 20009 12012 14017  9002 12013 31004 26008
 [205] 23010 26009 26010  8006 26011 26012 25003 18020 27001  8007 26013 30026
 [217]  8008 26014 26015  7010  7011  7114  9014 12014 23005 26071 29045 30027
 [229] 32004 26016  7012 30028  7013  8009 31005 14019 16011 31006  8010 12015
 [241] 16012 28005 19008 28006 14020 26017  7015 31007 22004 19009 26018  4010
 [253] 30029 32005 20011 15018  4001 13014 31008 21026 29006 21027  1003  8011
 [265] 28007 30007 30030 21028  4002 21099 14117 26019 10001  5005 20012  4011
 [277] 10002 32006 31009 31010     0 20247 15019 16013 26020 24005 27002 13015
 [289]  8012 30208  4003 30031  8013 28008 14021  5006 30157  7016 30032 24006
 [301] 21029 30033 24007 11007 31011 31012 27003 27004 19011 24008 30034 24009
 [313] 30054 31016 20025 20026 21045  7022 32009 15025 30055  4004  7023  7024
 [325] 31017 15026 31018 14030 13017 21046 13018  7025 15027 16021 24015 16022
 [337] 16023 31019  7026 16024  7027  7028 29010 15028 21047 21048 31021 21050
 [349]  7029 15029  7030 30056 15030 21049 30057 30058 31020 21051 21052 21053
 [361] 21054  8019 31022 21056 21055 12028 16025 21058 13019  7031 12029 15031
 [373] 14031 19013 30059 30060 21059 16026  8020 20027 14032 30062 31023 25007
 [385] 30063 16027 30064 31024 16028 16029 19012 20013 14022  7017 30035 24010
 [397] 24011 20014 28009 24013 15020 30036 16014 12016 30037 16015  7018 20015
 [409] 15021 21030 30038 17005 30039 21031 30040 12078 15022 12017 14024 16016
 [421] 30041 21032 21033 20016 16074  6002 30042 22005 14025  6003 27005 30043
 [433]  7019  3001 11009 18004 20018 14026 32007 20019 25004 10003 31013 20020
 [445] 16017 29018  7021 12018 12019 12020 16018  6004 30044  8014 21034 11010
 [457] 22006 11011 25005 30045 30046 30047  1004 20021 30048 20022 30049 16019
 [469] 21035 24014 30050  8015  9003 21036 15023 21037 12021 12022 30051 23001
 [481] 28010  9004 12023 12024 21038 29008  5007  9015  8017  6005 32008 21039
 [493] 13016 12025 21040 14027 15121 15024 14028 17006 21041 29009 21042 26022
 [505] 10004 11012 17007 12026 21043 30052 20023 30053 16020 25006 26023 31014
 [517] 27006 14029  8018 12027 20024 21044 31015 14033 18009  8021 26024 19014
 [529] 19015 19016 11013 11014 21060 15032  8022 10005 31025 31026 31027 31028
 [541] 31029 31030 31031 24016 15033 15034 16030 12075 14034 13009 14009 20010
 [553]  7014 29007 19010 20030 25010 14037 30205 14054  1010 28021 22011 24058
 [565] 10018 15064 32015  7070 14070 32041  8064 25008 20029 13020 21061 13021
 [577] 17008 27007 29046 30065 26025 14035  2001 21062 13022 16031 16032  4009
 [589]  5008 25009  7032 29012 21063 30066 31032 26026 14036 22007 23002 30067
 [601] 12030 30068  5009 13023  7033 21064 20032 32010  7034  7035  5010 26027
 [613] 16033  8023 19017 19018 32012 19020 12031  5011 32013 19021 21065 32014
 [625] 12032 32016 26070 10006 19022 19023 19024 19025  8025 14079 28011 10007
 [637] 28012  8026 26028 14038  8027 14039 24017 20034 20033 10008 21067  8029
 [649]  8028 19026 21066 32017 10009 11015 25011 26029  8030 20035 28013  8031
 [661]  5012 28014 20036  9005 28015 30069 31033  4005 21068 26030 20028 20039
 [673] 20043 20397 20549  8032  5013 10010 16034 19047 28016 30070 19028 31034
 [685] 31035 31036 21057  4006 14040 26031 18005 19029 29013 12033 16036 11016
 [697] 16037 32018 21069 26032 13024 26033 21070 30071 21071 20040 20041 13025
 [709] 30072 13026  7037 13027 21072 21073 21150 24018 15035  8033 21074 14041
 [721] 14042 13028 26034 16038 30073 21075 29014 15036 21076 21077 31037 13029
 [733] 30074 27008 22008 16039  7039 17009 21078 21079 12034 15037  7038  7040
 [745] 31038 30075  8034 12035 12036 30076 12081 26035 16040 10011 11017 16041
 [757] 15038 23003 30077 19030 21081 21082 12037 30078 30079  7042 30083 30080
 [769] 30082 30081 31039 30084 13030 20065 21083  7043 29015 30085  7044 15039
 [781] 15040 15041  7045 29016 21084 15042 14044 14045  6006 20042 18006 16042
 [793] 31040  9006  9007 21085 13031 16043 18007 30086 27009 30088 14046 27010
 [805] 32019 22009 21086 15044 30089 13032 30090 14047  8035 17010 11018 28017
 [817] 11019 32020 30091  1005 14048 15045 30093 14049 15046 32021  8036  5014
 [829] 16044 28018 16045  7046 15047  7047 17011 14050 15048 17012 21087 17013
 [841] 21088 27011 21089 15049 30169 12079 23006 16113 32022 21090 21091 21092
 [853] 12039 30094 14051 13033  7048  8037  5015 16046 19031 32023 30095 12080
 [865] 15050 14052  8038 16047 31041 31042 31043 31044 31045 30016 14018 26021
 [877] 20017  7020  8016  7036 16035 14043  7041  7050  9008 29048 21095 14057
 [889] 13040  3003 15070 20069 30127 16069 20076 20556  7099 12068 18019 21093
 [901] 14053 16048 24019  5016 19032 22010 30096  7049 30061  7052 30107  7075
 [913] 30132 16052 23007 29047 11020 12040 30097 10012 15051 21094 19033 28019
 [925] 13034 20044  8039  3009 32024 19003  3008 19027 19042 21118 16075 30137
 [937] 32025 15123 27012  8040 16049 20045 20046 20048 20049 20050 20051 20052
 [949] 20053 20562 20054 14055 26036 30098  8041 28020 15052 12041 30099 31046
 [961] 31047 30100  8042 11008  6007  7051 10013 16050  7115 16051 30101 19034
 [973] 20055 12077  7116 30102 12042 20056 14058  8043  8044  5017 28022 24020
 [985] 20057 24057 31048 31049 14059  7053 32026 21096  7054 26037 29017 17014
 [997] 20058 25012 30103 30104 30105 15053 19035 32027 28023  8045 31050 20037
[1009]  7055 13035 15054 12043 13037  2002 15055 24021 14060 32028 10014 14061
[1021] 17015 20059 30106 19036 28024 28025 32029  9016  9009 19037  6008 30108
[1033] 13051 13038 13039 28026 30109  7056 20060 13041 30110 21097 14062 12044
[1045] 31051 25013 24022 26038 13042 21098 30111 32030  5018 20061 32031  7117
[1057] 19038 19039 16053  8046  5019 15056 16054 32032  8047 11021  7057 31052
[1069] 32033 16055  3002 31053 29011 31054  5020 27013 26039 26040 26041  5021
[1081] 16056  8048 29021 30206 30112 30113 30013 20062 29023 15057 21100 30114
[1093] 21101  5022 26042 25018 20063 10015 21102 20064 15059 15058 21103 13043
[1105] 15060  7058 32034 16057 26043 30115 10016  8049 13044 15061 21104 32035
[1117]  8050 10039 28027 28028 16058 16059 20504 16060 20067  8051  5023 10017
[1129] 11022 16061 28029  7059  7060 21105 20068 14063 15062 21106  7061 15063
[1141] 17016  8052 32036 14064 12045 21107 30116 30117 12046 13045 26044 31055
[1153] 26045 26046 21108 30118  7062  7063 10019 30119 30120 23004 15065 15066
[1165] 15067  7064 31056 30121 15068  1006 13048 13047 28030 21109 16062 30122
[1177]  7065  4007 21110 28031 31057 16063 29024  7066  7067 21111 10020 30123
[1189] 32037 29041 15069 30124 16065 16064 27014 19040  5024 30126 30125 16066
[1201] 12047 22012 22013 16067 11023 10021 16068 30128 19041 12048 21112 31058
[1213] 21113  7068  5025 14065  7069 12049 22002 32038 20070 13049 26047 30129
[1225] 30130  2005 20071 10022 15071 14066 30131  8053 13050  5026 31059 21114
[1237]  7072 10023 11024 30133 17017 30134 23011 26048 14067 12050 16070 11025
[1249] 16071 20073 21115 12051 16072 22014 21116 31060 26049 16073 14069 30135
[1261] 21117 30136  5027  7073 15072 24023 26050 19043 20075  7074 20077 28032
[1273]  1007 30138 28033 32039 31061 24024  8054 10024 20078 11026  8055 18010
[1285]  8056 25014 26051 18011  7076 19044  5028 31062  5029 26052 16076 32040
[1297] 11027 20079 19045 24025 30139  5030  7077 25015 16079 11028 31063 20080
[1309] 20081 20082 20083 20084 20085 13036 20086 13052 20087 20088 21119 20089
[1321]  7118 20090 20091 20092 20093 20094 20095 20096 20097 30140 20098 20099
[1333] 30141 20100 20101 20102 20103 20104 20105 20106 21120 20107 20108 15073
[1345] 20109 20110 20111 24026 20112 20113 20114 20115 20121 13053 20122 20116
[1357] 20117 20118 20119 20120 20123 10025 20124 18012  5031 20125 28034 24027
[1369] 20126 20127 14071  7078 20128 20129 29049 14072 11029 21121 10026 20130
[1381] 20131 20132 20133 26053 15074 20134 13046 20135 21122 21123 20136 11030
[1393] 31065  7079 28035 20137 20138 20139 20140  8057  8058  1011 20141  8059
[1405] 11031 20142 20143 20144 20145 20146 20147 20148 20149 20150 20151 29050
[1417] 20152 21124 20153 14113 21125 14125 26072 25016 20154 20155 20156 20157
[1429] 20158 26054 20159 20160 20161 20162 20163 21126 20550 21127 29051 22015
[1441] 20164 20165 21128 20166  1008 20167 20072 15124 20168 20169 11032 20170
[1453] 21129 29052 20171 20172 21130 20173 21131 20175 20176 20177 20178 20179
[1465] 20180 20181 20183 20182 20184 20559 20185  7112 20191 20192 20186 20187
[1477] 20188 20189 20190 10027 20206 14073  5032 20193 10028 20194 22016 20195
[1489] 20196 30142 20197 20198 29053 20199 20200 20201 20202 20203 20204 20205
[1501] 20207 20208 20209 20210 20211 20212 20213 20214 20215 20216 20217 20218
[1513] 20219 20220 20221 20222 20223 20224 14007 14074 20226 29054 20227 20228
[1525] 20229 20230 20225 20231 20232 20233 29055 20234  7110 16077 12052 20235
[1537] 11033 10029 24028 26055 20236 20237 12053 14075 24029 14076 15075 20238
[1549] 14077 20239 20240 20241 20242 21132 20243 21133 20244 20245 15076 20246
[1561] 20248 20250 20251 20252 20253 20254 20255 20256 20249 20566 21134 20257
[1573] 20258 20259 20260 20261 20262 20264 20265 20263 11003 26056 20266 20267
[1585] 20268 14078 20269 20270 21135 20271 20272 20273 20274 20275 20276 20278
[1597] 20279 20281 20282 20283 20284 20285 20286 12054 20287 21136 20288 21137
[1609] 19046 21138 20290 24030 20289 28036 21139 20291 20292 29025 20293 20294
[1621] 20295 20296 20297 20298 20299 20300 20301 20302 20303 21140 20305 20304
[1633] 26057 10030 20306 19019 20307 20308 20309 20310 20311 20312 20313 20314
[1645] 18013 20316 20317 20315 20318 20319 20320 20321 20322 20323 20324 20325
[1657] 20326 20327 20328 20329 20330 20331 14098 20332 20333 20337 20339 20340
[1669] 20335 21141 20336 20341  5033 30211 20342 21142 21143 21144 13054 20343
[1681] 20344 14080 20345 20346 20347 20348 20349 21145 20350 20351 15077 20352
[1693] 20534 20535 20536 24034 31064 29020 20354 20355 20356 16078 29056 20357
[1705] 20358 20359 20360 20353 26058 29057  8060 20361 29058 20362 20363 20364
[1717] 20365 20366 20367 20368 20369 20074 20370 20371 21146 20372 20373 11034
[1729] 19048 24031 10031 20374 20375 20376 11035 20377 20378 20379 20380 29059
[1741] 20381 20382 20383 29026 20384 20385 20386 26059 31066 20387 21147 20569
[1753] 20388 20389 21148 29060  8024 20390 20391 20392 20393 20047 20394 20395
[1765] 20399 20400 20404 20406 20407 20401 20402 20403 32058 14081 14056 18014
[1777] 24032 20408 20409 20410 20411 20412 20413 20414 20415 20416 20417 20418
[1789] 20419 20396 20420 20421 20422 20423 20424 20425 20426 20427 20428 20429
[1801] 20430 20431 20432 20433 20434 20435 20436 20437 20438 20439 20440 20441
[1813] 20442 20443 20444 20445 20446 20447 20448 20449 20450 20451 20452 20453
[1825] 20454 20455 20456 20457 20459 20460 20458 13055 20461  7119 20462 20463
[1837] 20464 20465 18015 20466 20467 20468 20469 20470 20471 20472 20473 20474
[1849] 11036 20475 21149 20476 20477 20478 20479 20066 20480 20481 10032 20482
[1861] 30212 20483 20484 20485 20487 20488 20489 20490 20491 20492 20493 20494
[1873] 13056 30143 20495 20496 20497 20498 20499 20500 20501 20502 20503 19049
[1885] 20506 20507 20508 20509 20505 20510 20511 20512 20513 20514 20515 20516
[1897] 20517 20518 20519 20520 20521 20522 20523 20524 20525 24033 21151 20530
[1909] 20531 20532 20533 15078 20526 20527 20528 20529 26060  8061  8062 30144
[1921] 14082 16080 31067  5034 20537 11037  7080  7081 25017 31068 13057  7082
[1933] 20538 30146  7083 30145 30147 30148 24035 20539 23008  7084 21152 32042
[1945] 30149 28037 31069  7085 15079 26061 26062  7086  7087 10033 31070 31071
[1957] 15080 31072  7088 32043 16081 32044 16082 27015 31073 31074 14083 14084
[1969] 30150 24036 14085 10034 20031 24037 30151 24038 24039 30152 28038 24040
[1981] 24012 16083 30153 20541 16084 16085 16086 20542 24041 24042 30154 30155
[1993]  7089  7090 14086  7091 11038 16087 16088 11039 13058 30209 20543 30156
[2005] 12055 31075 27016 21153 14087 15081 21154  2003 14089 12056 31076 14088
[2017] 30158  6009 21155 13059 12057  7092 18016 21156 30159 21157 15082 31077
[2029] 31078 31079 31080 31081 31082 31083 12058 15083 15084 15085 15086 31084
[2041] 17018 17033 15087  8063 31085 30161  4008 14090 30162 21158 15088 29027
[2053] 13060 15089 15090  7093 30163 27017 14091 30164 20544 14092 29028 15091
[2065] 21159  7094 15092 20545 20546 21160 20547 26063 31086 16089 17019 21161
[2077] 21162 14093 21163 30165 21164 13061 32045 12059 13062 10035 13063 20548
[2089] 21165 21166 29019 13064 30166 15093 15094 32046 21167 30167 21168 21169
[2101] 21171 21170 29029  1009 18017 15095 17020 14094 30168 22017 15096 29030
[2113] 17021 21172 17022 21173 13065 12060 31087 29031 29032 14095 32047 15097
[2125] 15098 30170 15099 30171 30172 31088 21174 30173 13067 15100 21175 15101
[2137] 13068 31089 11040 30174 24043 30175  2004  7096 21176 15102 31090 16090
[2149] 16091 31091 16092 31092 31093 31094 31095 12061 14096 13069 31096 30180
[2161] 21179 12062 12063 30176 20551 30177 30178 21177 30179 20552 21178  9011
[2173] 10036 21180 13070 13071 14097 12064 30181 20553 12065 15103 30182 15104
[2185] 17023  9012 16093 32048 21181 30024 17024 13072 13073 21182 21183 12066
[2197] 21184 30183 21185 12067 30184 17025 21186 15105 29033 21187 29034 13074
[2209] 17026 16094 30185 29035 21188 21189 16095 13075 14099 22018 15106 14100
[2221] 30186  7097 14101 15125 15107 14102 30187 14103 10037  5035 14104 29036
[2233]  7098 17027 21190 20554 14105 30188 32057 30207 26064 32011 20555 26065
[2245] 13076 28039 13077 21191 15108 15109 23009  7100 16096 31097 16097 14106
[2257] 14107 14108 16098 18018 30189 30190  7102  7101 21192  7103 16099 21193
[2269]  7104 16100 16101 29038 31098 31099 31100 31101 14109 14110 20557  7105
[2281] 26066 11041  8065 30191  8066 16102 30210 20558 31102 15110 15122 14111
[2293] 14112 11042  8067 28040 19050 32049 24044 30192 24045  9017  7106 16103
[2305] 21194 30193 32050 10038 21195 11043 28041  5036 30194  7071 14114  7107
[2317] 15111  6010 24056 24046 20405 32051 20338 24047 24048 24049 24050 20540
[2329] 13066 20334 20565 15112 20560 32052 32053 14115 15113 14116 20038 24051
[2341] 26067 32054 24052 26068 14068 20277 20280 20486  5037 15114  7108 11044
[2353] 28042 19051 16104 32055 16105 30087 15043 18008 29039 12069 29040 21196
[2365] 11045 30092 29042 28043 21197 21198 24054 21199 31103 21200 13078 13079
[2377] 12070 21201  9013 12071 17028 21202 21203 15115 30195 14118 13080  7109
[2389] 30196 21204 29043 17029 31104 20561 31105 17030 26069 30197 21205 31106
[2401] 20563 16106 11046 20564 21206 21207 16107 32056 29044 17031 21208 15116
[2413] 14119 15117 17032 30198 13081 16108 14120 14121 21210 14122 20567 20568
[2425] 12072 21209 13082 14123 14023 14124  5038 21211 24055 30199 21212 13083
[2437] 30200 12038 21213 29037 13084 20570  7111 15118 21214 16109 16110 16111
[2449] 12073 16112 12074 30201 21215 30202 21216 21217 30203 15119 15120
[1]     NA 211150  16146

Call:
   felm(formula = unique_groups ~ stag_ind_bin + party_alt + population +      MajorHighway + MajorPort + Airports + Railline + Oilline +      Intlborder + Shoreline + pri_mayor | Bin + State | 0 | State,      data = poppies_esberg) 

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0894 -0.4512 -0.0671  0.2481  5.5824 

Coefficients:
               Estimate Cluster s.e. t value Pr(>|t|)  
stag_ind_bin  2.881e-01    2.748e-01   1.048   0.3251  
party_alt    -2.385e-01    2.946e-01  -0.809   0.4417  
population    9.214e-07    1.792e-06   0.514   0.6211  
MajorHighway -1.751e-01    1.017e-01  -1.721   0.1235  
MajorPort           NaN    0.000e+00     NaN      NaN  
Airports      3.138e-01    1.555e-01   2.017   0.0784 .
Railline     -2.173e-02    2.818e-01  -0.077   0.9404  
Oilline      -1.008e+00    5.745e-01  -1.755   0.1173  
Intlborder    1.768e+00    1.166e+00   1.516   0.1679  
Shoreline     2.722e-03    8.088e-02   0.034   0.9740  
pri_mayor     3.666e-01    2.513e-01   1.459   0.1828  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.231 on 141 degrees of freedom
  (3 observations deleted due to missingness)
Multiple R-squared(full model): 0.6201   Adjusted R-squared: 0.5662 
Multiple R-squared(proj model):  0.16   Adjusted R-squared: 0.04082 
F-statistic(full model, *iid*):11.51 on 20 and 141 DF, p-value: < 2.2e-16 
F-statistic(proj model):  9968 on 11 and 8 DF, p-value: 2.689e-15 



Call:
   felm(formula = unique_groups ~ stag_ind_bin + party_alt + population +      MajorHighway + MajorPort + Airports + Railline + Oilline +      Intlborder + Shoreline + pri_mayor | Bin + State | 0 | State,      data = np_esberg) 

Residuals:
    Min      1Q  Median      3Q     Max 
-4.8174 -0.4719 -0.1111  0.1647 12.9413 

Coefficients:
               Estimate Cluster s.e. t value Pr(>|t|)    
stag_ind_bin  4.628e-01    2.585e-01   1.791 0.095014 .  
party_alt    -1.473e-01    1.197e-01  -1.231 0.238662    
population    3.736e-06    1.128e-06   3.313 0.005128 ** 
MajorHighway  8.393e-03    6.474e-02   0.130 0.898687    
MajorPort     5.375e-01    8.808e-01   0.610 0.551470    
Airports      4.874e-01    3.376e-01   1.443 0.170904    
Railline      8.475e-01    1.786e-01   4.746 0.000313 ***
Oilline      -6.190e-01    1.346e-01  -4.601 0.000412 ***
Intlborder    2.757e-01    6.007e-01   0.459 0.653294    
Shoreline    -1.610e-01    1.559e-01  -1.033 0.319137    
pri_mayor    -1.322e-02    8.000e-02  -0.165 0.871110    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.188 on 1374 degrees of freedom
  (275 observations deleted due to missingness)
Multiple R-squared(full model): 0.4032   Adjusted R-squared: 0.3915 
Multiple R-squared(proj model): 0.1329   Adjusted R-squared: 0.1158 
F-statistic(full model, *iid*):34.38 on 27 and 1374 DF, p-value: < 2.2e-16 
F-statistic(proj model): 42.93 on 11 and 14 DF, p-value: 7.585e-09 


\begin{table}
\centering
\begin{talltblr}[         %% tabularray outer open
caption={Treatment Effect on Criminal Organizations in Criminally-Entrenched Areas and non-Entrenched Areas},
note{}={Significance levels: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.},
]                     %% tabularray outer close
{                     %% tabularray inner open
colspec={Q[]Q[]Q[]},
column{2-3}={}{halign=c,},
column{1}={}{halign=l,},
hline{4}={1-3}{solid, black, 0.05em},
}                     %% tabularray inner close
\toprule
& Unique Groups, CE & Unique Groups \\ \midrule %% TinyTableHeader
Mayoral Reelection & \num{0.288} & \num{0.463}+ \\
& (\num{0.275}) & (\num{0.258}) \\
Num.Obs. & \num{162} & \num{1402} \\
R2 & \num{0.620} & \num{0.403} \\
\bottomrule
\end{talltblr}
\end{table} 
\begin{table}
\centering
\begin{talltblr}[         %% tabularray outer open
caption={Treatment Effect on Criminal Organizations in Criminally-Entrenched Areas and non-Entrenched Areas},
note{}={Significance levels: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.},
]                     %% tabularray outer close
{                     %% tabularray inner open
colspec={Q[]Q[]Q[]},
column{2-3}={}{halign=c,},
column{1}={}{halign=l,},
hline{4}={1-3}{solid, black, 0.05em},
}                     %% tabularray inner close
\toprule
& DV, CE & DV \\ \midrule %% TinyTableHeader
Mayoral Reelection & \num{0.000} & \num{0.137}* \\
& (\num{0.070}) & (\num{0.060}) \\
Num.Obs. & \num{162} & \num{1402} \\
R2 & \num{0.370} & \num{0.195} \\
\bottomrule
\end{talltblr}
\end{table} 

Call:
   felm(formula = monopoly ~ stag_ind_bin + population + MajorHighway +      MajorPort + Airports + Railline + Oilline + Intlborder +      Shoreline + pri_mayor + party_alt | Bin + State | 0 | State,      data = poppies_esberg) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.58351 -0.19293 -0.03238  0.08740  0.90694 

Coefficients:
               Estimate Cluster s.e. t value Pr(>|t|)    
stag_ind_bin  6.294e-02    3.858e-02   1.632 0.141398    
population   -6.224e-07    4.113e-07  -1.513 0.168682    
MajorHighway  4.130e-02    1.289e-01   0.320 0.756944    
MajorPort           NaN    0.000e+00     NaN      NaN    
Airports      1.380e-01    9.219e-02   1.497 0.172813    
Railline      2.613e-01    4.381e-02   5.964 0.000337 ***
Oilline      -2.118e-01    2.952e-02  -7.173 9.49e-05 ***
Intlborder    1.384e-02    2.579e-01   0.054 0.958511    
Shoreline     2.330e-02    3.307e-02   0.705 0.501106    
pri_mayor     1.337e-01    8.574e-02   1.559 0.157560    
party_alt     1.509e-02    7.586e-02   0.199 0.847290    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.322 on 141 degrees of freedom
  (3 observations deleted due to missingness)
Multiple R-squared(full model): 0.2849   Adjusted R-squared: 0.1835 
Multiple R-squared(proj model): 0.09775   Adjusted R-squared: -0.03023 
F-statistic(full model, *iid*):2.809 on 20 and 141 DF, p-value: 0.0002082 
F-statistic(proj model): 1.478e+04 on 11 and 8 DF, p-value: 5.564e-16 



Call:
   felm(formula = monopoly ~ stag_ind_bin + population + MajorHighway +      MajorPort + Airports + Railline + Oilline + Intlborder +      Shoreline + pri_mayor + party_alt | Bin + State | 0 | State,      data = np_esberg) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.34492 -0.07934 -0.01460 -0.00219  1.00409 

Coefficients:
               Estimate Cluster s.e. t value Pr(>|t|)    
stag_ind_bin -4.113e-02    4.200e-02  -0.979    0.344    
population    9.957e-08    8.079e-08   1.232    0.238    
MajorHighway  1.128e-03    1.676e-02   0.067    0.947    
MajorPort     1.641e-01    1.323e-01   1.240    0.235    
Airports     -4.015e-02    3.214e-02  -1.249    0.232    
Railline     -1.402e-01    2.061e-02  -6.802 8.58e-06 ***
Oilline       1.469e-01    1.071e-02  13.711 1.66e-09 ***
Intlborder   -9.447e-03    1.860e-02  -0.508    0.619    
Shoreline     1.226e-02    2.830e-02   0.433    0.672    
pri_mayor     3.576e-03    1.477e-02   0.242    0.812    
party_alt    -3.666e-02    2.108e-02  -1.739    0.104    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2081 on 1374 degrees of freedom
  (275 observations deleted due to missingness)
Multiple R-squared(full model): 0.08071   Adjusted R-squared: 0.06264 
Multiple R-squared(proj model): 0.02046   Adjusted R-squared: 0.001207 
F-statistic(full model, *iid*):4.468 on 27 and 1374 DF, p-value: 4.064e-13 
F-statistic(proj model):  1010 on 11 and 14 DF, p-value: < 2.2e-16 


\begin{table}
\centering
\begin{talltblr}[         %% tabularray outer open
caption={Treatment Effect on Criminal Organizations in Criminally-Entrenched Areas and non-Entrenched Areas},
note{}={Significance levels: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.},
]                     %% tabularray outer close
{                     %% tabularray inner open
colspec={Q[]Q[]Q[]},
column{2-3}={}{halign=c,},
column{1}={}{halign=l,},
hline{4}={1-3}{solid, black, 0.05em},
}                     %% tabularray inner close
\toprule
& DV, CE & DV \\ \midrule %% TinyTableHeader
Mayoral Reelection & \num{0.063} & \num{-0.041} \\
& (\num{0.039}) & (\num{0.042}) \\
Num.Obs. & \num{162} & \num{1402} \\
R2 & \num{0.285} & \num{0.081} \\
\bottomrule
\end{talltblr}
\end{table} 
\begin{table}
\centering
\begin{talltblr}[         %% tabularray outer open
caption={Treatment Effect on Criminal Organizations in Criminally-Entrenched Areas and non-Entrenched Areas},
note{}={Significance levels: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.},
]                     %% tabularray outer close
{                     %% tabularray inner open
colspec={Q[]Q[]Q[]},
column{2-3}={}{halign=c,},
column{1}={}{halign=l,},
hline{4}={1-3}{solid, black, 0.05em},
}                     %% tabularray inner close
\toprule
& DV, CE & DV \\ \midrule %% TinyTableHeader
Mayoral Reelection & \num{0.073} & \num{0.104}+ \\
& (\num{0.061}) & (\num{0.058}) \\
Num.Obs. & \num{162} & \num{1402} \\
R2 & \num{0.376} & \num{0.271} \\
\bottomrule
\end{talltblr}
\end{table} 

   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
1444  969 1133  989 1493 1395  954  956 5077 1304  975  457 1109  939 1414  968 
  17   18   19   20   21   22   23   24   25   26   27   28   29   30   31   32 
1375 1014 1341  972  941 1124  942  989  945 1577  940 1364 1012  928  945  981 
# A tibble: 0 × 3
# ℹ 3 variables: ADM2_PCODE <int>, year <dbl>, n <int>
[1] 2.152317
[1] 2.216411

Call:
   felm(formula = corrup_muni ~ stag_ind_bin + party_alt + population +      MajorHighway + MajorPort + Airports + Railline + Oilline +      Intlborder + Shoreline + pri_mayor + Pres_misalign + state_misalign |      Bin + State | 0 | State, data = poppies_panel3) 

Residuals:
      Min        1Q    Median        3Q       Max 
-0.151235 -0.001994  0.000000  0.001994  0.151235 

Coefficients:
                 Estimate Cluster s.e. t value Pr(>|t|)  
stag_ind_bin    2.452e-01    9.774e-02   2.509   0.0870 .
party_alt       4.815e-01    1.493e-01   3.224   0.0484 *
population      1.323e-06    1.216e-06   1.088   0.3562  
MajorHighway          NaN    0.000e+00     NaN      NaN  
MajorPort             NaN    0.000e+00     NaN      NaN  
Airports              NaN    0.000e+00     NaN      NaN  
Railline              NaN    0.000e+00     NaN      NaN  
Oilline        -6.261e-01    5.697e-01  -1.099   0.3521  
Intlborder     -1.533e+00    7.095e-01  -2.161   0.1195  
Shoreline             NaN    0.000e+00     NaN      NaN  
pri_mayor      -3.646e-01    1.107e-01  -3.293   0.0460 *
Pres_misalign   4.929e-01    1.210e-01   4.074   0.0267 *
state_misalign -4.902e-03    1.198e-01  -0.041   0.9699  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2236 on 3 degrees of freedom
  (885 observations deleted due to missingness)
Multiple R-squared(full model): 0.9088   Adjusted R-squared: 0.3919 
Multiple R-squared(proj model): 0.7736   Adjusted R-squared: -0.509 
F-statistic(full model, *iid*):1.758 on 17 and 3 DF, p-value: 0.3569 
F-statistic(proj model): 1.546e+04 on 13 and 3 DF, p-value: 7.592e-07 



Call:
   felm(formula = corrup_muni ~ stag_ind_bin + party_alt + population +      MajorHighway + MajorPort + Airports + Railline + Oilline +      Intlborder + Shoreline + pri_mayor + Pres_misalign + state_misalign |      Bin + State | 0 | State, data = np_panel3) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.99660 -0.24933 -0.03799  0.19839  2.51103 

Coefficients:
                 Estimate Cluster s.e. t value Pr(>|t|)    
stag_ind_bin    7.053e-01    5.592e-02  12.613 2.67e-13 ***
party_alt      -2.639e-02    4.457e-02  -0.592    0.558    
population     -6.488e-08    8.203e-08  -0.791    0.435    
MajorHighway    6.492e-02    1.608e-01   0.404    0.689    
MajorPort      -1.839e-01    2.093e-01  -0.879    0.387    
Airports       -1.816e-02    4.910e-02  -0.370    0.714    
Railline        7.629e-02    2.310e-01   0.330    0.744    
Oilline        -1.547e-01    2.338e-01  -0.662    0.513    
Intlborder     -1.716e-01    1.187e-01  -1.446    0.159    
Shoreline      -3.455e-02    2.082e-01  -0.166    0.869    
pri_mayor      -1.111e-01    7.888e-02  -1.409    0.169    
Pres_misalign  -6.500e-03    1.423e-01  -0.046    0.964    
state_misalign  1.219e-01    9.356e-02   1.303    0.203    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.4505 on 269 degrees of freedom
  (6588 observations deleted due to missingness)
Multiple R-squared(full model): 0.3465   Adjusted R-squared: 0.242 
Multiple R-squared(proj model): 0.05477   Adjusted R-squared: -0.09632 
F-statistic(full model, *iid*):3.317 on 43 and 269 DF, p-value: 1.346e-09 
F-statistic(proj model): 36.99 on 13 and 29 DF, p-value: 1.65e-14 



Call:
   felm(formula = corrup_muni ~ stag_ind_bin + MajorHighway + population +      MajorPort + Airports + Railline + Oilline + Intlborder +      Shoreline + pri_mayor + Pres_misalign + state_misalign |      Bin + State | 0 | State, data = test_merge) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.98322 -0.24535 -0.04761  0.19690  2.52252 

Coefficients:
                 Estimate Cluster s.e. t value Pr(>|t|)  
stag_ind_bin    2.564e-01    2.281e-01   1.124   0.2699  
MajorHighway    7.360e-02    1.550e-01   0.475   0.6383  
population     -8.016e-08    7.567e-08  -1.059   0.2979  
MajorPort      -1.283e-01    1.679e-01  -0.765   0.4505  
Airports       -7.884e-03    4.635e-02  -0.170   0.8661  
Railline        9.462e-02    1.439e-01   0.657   0.5160  
Oilline        -1.299e-01    1.496e-01  -0.868   0.3923  
Intlborder     -2.181e-01    1.052e-01  -2.074   0.0468 *
Shoreline      -2.209e-02    1.819e-01  -0.121   0.9042  
pri_mayor      -1.027e-01    7.415e-02  -1.384   0.1764  
Pres_misalign  -2.792e-02    1.223e-01  -0.228   0.8210  
state_misalign  1.185e-01    8.521e-02   1.391   0.1744  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.4375 on 302 degrees of freedom
  (7461 observations deleted due to missingness)
Multiple R-squared(full model): 0.3354   Adjusted R-squared: 0.2408 
Multiple R-squared(proj model): 0.04153   Adjusted R-squared: -0.09494 
F-statistic(full model, *iid*):3.545 on 43 and 302 DF, p-value: 6.944e-11 
F-statistic(proj model): 1.541 on 12 and 30 DF, p-value: 0.1639 


\begin{table}
\centering
\begin{talltblr}[         %% tabularray outer open
caption={Treatment Effect on Corruption Measures in Criminally-Entrenched Areas and non-Entrenched Areas},
note{}={Significance levels: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.},
]                     %% tabularray outer close
{                     %% tabularray inner open
colspec={Q[]Q[]Q[]Q[]},
column{2-4}={}{halign=c,},
column{1}={}{halign=l,},
hline{4}={1-4}{solid, black, 0.05em},
}                     %% tabularray inner close
\toprule
& CE & Non CE & National \\ \midrule %% TinyTableHeader
Mayoral Reelection & \num{0.245}+ & \num{0.705}*** & \num{0.256} \\
& (\num{0.098}) & (\num{0.056}) & (\num{0.228}) \\
Num.Obs. & \num{21} & \num{313} & \num{346} \\
R2 & \num{0.909} & \num{0.346} & \num{0.335} \\
\bottomrule
\end{talltblr}
\end{table} 
[1] 1.708276
[1] 1.752415

Call:
   felm(formula = corrup_police ~ stag_ind_bin + party_alt + population +      MajorHighway + MajorPort + Airports + Railline + Oilline +      Intlborder + Shoreline + pri_mayor + Pres_misalign + state_misalign |      Bin + State | 0 | State, data = poppies_panel3) 

Residuals:
    Min      1Q  Median      3Q     Max 
-0.1200 -0.0273  0.0000  0.0273  0.1200 

Coefficients:
                 Estimate Cluster s.e. t value Pr(>|t|)
stag_ind_bin   -1.829e-01    9.036e-02  -2.024    0.136
party_alt       1.699e-01    1.148e-01   1.480    0.236
population     -2.873e-07    8.024e-07  -0.358    0.744
MajorHighway          NaN    0.000e+00     NaN      NaN
MajorPort             NaN    0.000e+00     NaN      NaN
Airports              NaN    0.000e+00     NaN      NaN
Railline              NaN    0.000e+00     NaN      NaN
Oilline         9.285e-02    3.864e-01   0.240    0.826
Intlborder     -3.977e-01    4.747e-01  -0.838    0.464
Shoreline             NaN    0.000e+00     NaN      NaN
pri_mayor      -1.355e-01    1.052e-01  -1.288    0.288
Pres_misalign   5.182e-02    1.256e-01   0.413    0.708
state_misalign  1.564e-01    9.197e-02   1.701    0.187

Residual standard error: 0.1523 on 3 degrees of freedom
  (885 observations deleted due to missingness)
Multiple R-squared(full model): 0.9296   Adjusted R-squared: 0.5309 
Multiple R-squared(proj model): 0.8233   Adjusted R-squared: -0.178 
F-statistic(full model, *iid*):2.332 on 17 and 3 DF, p-value: 0.2651 
F-statistic(proj model): 11.08 on 13 and 3 DF, p-value: 0.03589 



Call:
   felm(formula = corrup_police ~ stag_ind_bin + party_alt + population +      MajorHighway + MajorPort + Airports + Railline + Oilline +      Intlborder + Shoreline + pri_mayor + Pres_misalign + state_misalign |      Bin + State | 0 | State, data = np_panel3) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.67615 -0.17747 -0.03183  0.13810  1.88335 

Coefficients:
                 Estimate Cluster s.e. t value Pr(>|t|)    
stag_ind_bin    3.313e-01    1.772e-02  18.702   <2e-16 ***
party_alt      -3.984e-02    2.838e-02  -1.404   0.1710    
population     -8.524e-08    4.230e-08  -2.015   0.0532 .  
MajorHighway    1.397e-01    7.948e-02   1.758   0.0892 .  
MajorPort      -3.296e-02    1.083e-01  -0.304   0.7631    
Airports        9.417e-03    3.040e-02   0.310   0.7589    
Railline       -7.287e-02    9.582e-02  -0.760   0.4531    
Oilline        -2.711e-02    9.759e-02  -0.278   0.7832    
Intlborder     -3.046e-02    6.792e-02  -0.449   0.6571    
Shoreline      -1.373e-02    9.151e-02  -0.150   0.8817    
pri_mayor      -7.370e-02    6.408e-02  -1.150   0.2595    
Pres_misalign  -5.494e-03    8.637e-02  -0.064   0.9497    
state_misalign  8.251e-02    5.305e-02   1.555   0.1307    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.3271 on 269 degrees of freedom
  (6588 observations deleted due to missingness)
Multiple R-squared(full model): 0.3065   Adjusted R-squared: 0.1956 
Multiple R-squared(proj model): 0.05448   Adjusted R-squared: -0.09666 
F-statistic(full model, *iid*):2.765 on 43 and 269 DF, p-value: 3.465e-07 
F-statistic(proj model): 48.52 on 13 and 29 DF, p-value: 4.221e-16 



Call:
   felm(formula = corrup_police ~ stag_ind_bin + MajorHighway +      population + MajorPort + Airports + Railline + Oilline +      Intlborder + Shoreline + pri_mayor + Pres_misalign + state_misalign |      Bin + State | 0 | State, data = test_merge) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.65530 -0.16815 -0.03706  0.13721  1.88392 

Coefficients:
                 Estimate Cluster s.e. t value Pr(>|t|)   
stag_ind_bin    1.610e-01    5.791e-02   2.780  0.00929 **
MajorHighway    1.399e-01    7.593e-02   1.842  0.07534 . 
population     -1.014e-07    4.040e-08  -2.510  0.01769 * 
MajorPort      -3.226e-02    8.874e-02  -0.364  0.71871   
Airports        7.005e-03    2.909e-02   0.241  0.81133   
Railline       -8.247e-03    6.395e-02  -0.129  0.89825   
Oilline        -6.926e-02    6.894e-02  -1.005  0.32312   
Intlborder     -9.044e-02    7.265e-02  -1.245  0.22286   
Shoreline       7.012e-03    9.459e-02   0.074  0.94139   
pri_mayor      -6.762e-02    6.070e-02  -1.114  0.27412   
Pres_misalign  -2.817e-02    8.022e-02  -0.351  0.72793   
state_misalign  7.489e-02    5.035e-02   1.487  0.14735   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.3154 on 302 degrees of freedom
  (7461 observations deleted due to missingness)
Multiple R-squared(full model):   0.3   Adjusted R-squared: 0.2003 
Multiple R-squared(proj model): 0.05336   Adjusted R-squared: -0.08142 
F-statistic(full model, *iid*): 3.01 on 43 and 302 DF, p-value: 1.906e-08 
F-statistic(proj model): 3.098 on 12 and 30 DF, p-value: 0.005894 


\begin{table}
\centering
\begin{talltblr}[         %% tabularray outer open
caption={Treatment Effect on Corruption Measures in Criminally-Entrenched Areas and non-Entrenched Areas},
note{}={Significance levels: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.},
]                     %% tabularray outer close
{                     %% tabularray inner open
colspec={Q[]Q[]Q[]Q[]},
column{2-4}={}{halign=c,},
column{1}={}{halign=l,},
hline{4}={1-4}{solid, black, 0.05em},
}                     %% tabularray inner close
\toprule
& CE & Non CE & National \\ \midrule %% TinyTableHeader
Mayoral Reelection & \num{-0.183} & \num{0.331}*** & \num{0.161}** \\
& (\num{0.090}) & (\num{0.018}) & (\num{0.058}) \\
Num.Obs. & \num{21} & \num{313} & \num{346} \\
R2 & \num{0.930} & \num{0.307} & \num{0.300} \\
\bottomrule
\end{talltblr}
\end{table} 
[1] 3.824166
[1] 3.778381

Call:
   felm(formula = satisfaction_police ~ stag_ind_bin + party_alt +      population + MajorHighway + MajorPort + Airports + Railline +      Oilline + Intlborder + Shoreline + pri_mayor + Pres_misalign +      state_misalign | Bin + State | 0 | State, data = poppies_panel3) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.09888 -0.04944  0.00000  0.04944  0.09888 

Coefficients:
                 Estimate Cluster s.e. t value Pr(>|t|)
stag_ind_bin    1.788e-01    1.334e-01   1.340    0.273
party_alt       4.440e-02    2.150e-01   0.207    0.850
population      1.417e-06    1.435e-06   0.987    0.396
MajorHighway          NaN    0.000e+00     NaN      NaN
MajorPort             NaN    0.000e+00     NaN      NaN
Airports              NaN    0.000e+00     NaN      NaN
Railline              NaN    0.000e+00     NaN      NaN
Oilline        -7.143e-01    7.799e-01  -0.916    0.427
Intlborder      2.437e-03    8.474e-01   0.003    0.998
Shoreline             NaN    0.000e+00     NaN      NaN
pri_mayor       9.075e-02    1.353e-01   0.671    0.550
Pres_misalign   1.407e-01    2.904e-01   0.484    0.661
state_misalign -1.515e-01    2.003e-01  -0.756    0.504

Residual standard error: 0.1671 on 3 degrees of freedom
  (885 observations deleted due to missingness)
Multiple R-squared(full model): 0.9793   Adjusted R-squared: 0.8618 
Multiple R-squared(proj model): 0.8894   Adjusted R-squared: 0.263 
F-statistic(full model, *iid*):8.339 on 17 and 3 DF, p-value: 0.05287 
F-statistic(proj model): 2.139 on 13 and 3 DF, p-value: 0.29 



Call:
   felm(formula = satisfaction_police ~ stag_ind_bin + party_alt +      population + MajorHighway + MajorPort + Airports + Railline +      Oilline + Intlborder + Shoreline + pri_mayor + Pres_misalign +      state_misalign | Bin + State | 0 | State, data = np_panel3) 

Residuals:
     Min       1Q   Median       3Q      Max 
-1.51029 -0.26260  0.00306  0.26318  1.85923 

Coefficients:
                 Estimate Cluster s.e. t value Pr(>|t|)    
stag_ind_bin    4.016e-01    5.319e-02   7.550 2.54e-08 ***
party_alt       7.156e-02    9.212e-02   0.777  0.44359    
population      1.913e-07    6.101e-08   3.135  0.00391 ** 
MajorHighway   -1.322e-01    1.320e-01  -1.001  0.32503    
MajorPort      -2.553e-01    1.280e-01  -1.994  0.05566 .  
Airports       -3.263e-02    4.766e-02  -0.685  0.49904    
Railline        1.348e-02    1.277e-01   0.106  0.91668    
Oilline         1.317e-01    1.399e-01   0.942  0.35411    
Intlborder     -2.082e-02    1.185e-01  -0.176  0.86175    
Shoreline       7.214e-02    1.846e-01   0.391  0.69885    
pri_mayor      -2.612e-02    8.111e-02  -0.322  0.74977    
Pres_misalign   1.048e-01    1.227e-01   0.854  0.40008    
state_misalign  7.896e-02    7.672e-02   1.029  0.31194    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.469 on 269 degrees of freedom
  (6588 observations deleted due to missingness)
Multiple R-squared(full model): 0.4762   Adjusted R-squared: 0.3925 
Multiple R-squared(proj model): 0.0654   Adjusted R-squared: -0.08399 
F-statistic(full model, *iid*):5.688 on 43 and 269 DF, p-value: < 2.2e-16 
F-statistic(proj model): 20.63 on 13 and 29 DF, p-value: 3.297e-11 



Call:
   felm(formula = satisfaction_police ~ stag_ind_bin + MajorHighway +      population + MajorPort + Airports + Railline + Oilline +      Intlborder + Shoreline + pri_mayor + Pres_misalign + state_misalign |      Bin + State | 0 | State, data = test_merge) 

Residuals:
     Min       1Q   Median       3Q      Max 
-1.53151 -0.24284 -0.00287  0.25254  1.91666 

Coefficients:
                 Estimate Cluster s.e. t value Pr(>|t|)   
stag_ind_bin   -7.629e-02    2.619e-01  -0.291  0.77285   
MajorHighway   -9.455e-02    1.326e-01  -0.713  0.48146   
population      1.788e-07    5.868e-08   3.047  0.00479 **
MajorPort      -2.238e-01    1.020e-01  -2.195  0.03605 * 
Airports       -2.793e-02    4.143e-02  -0.674  0.50544   
Railline       -5.246e-02    1.504e-01  -0.349  0.72962   
Oilline         1.913e-01    1.405e-01   1.361  0.18353   
Intlborder      7.222e-02    1.209e-01   0.597  0.55491   
Shoreline       2.385e-02    1.491e-01   0.160  0.87402   
pri_mayor      -7.524e-03    7.560e-02  -0.100  0.92139   
Pres_misalign   9.377e-02    1.151e-01   0.814  0.42181   
state_misalign  6.829e-02    7.307e-02   0.935  0.35745   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.4551 on 302 degrees of freedom
  (7461 observations deleted due to missingness)
Multiple R-squared(full model): 0.473   Adjusted R-squared: 0.398 
Multiple R-squared(proj model): 0.06137   Adjusted R-squared: -0.07227 
F-statistic(full model, *iid*):6.305 on 43 and 302 DF, p-value: < 2.2e-16 
F-statistic(proj model): 8.934 on 12 and 30 DF, p-value: 6.164e-07 


\begin{table}
\centering
\begin{talltblr}[         %% tabularray outer open
caption={Treatment Effect on Corruption Measures in Criminally-Entrenched Areas and non-Entrenched Areas},
note{}={Significance levels: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.},
]                     %% tabularray outer close
{                     %% tabularray inner open
colspec={Q[]Q[]Q[]Q[]},
column{2-4}={}{halign=c,},
column{1}={}{halign=l,},
hline{4}={1-4}{solid, black, 0.05em},
}                     %% tabularray inner close
\toprule
& CE & Non CE & National \\ \midrule %% TinyTableHeader
Mayoral Reelection & \num{0.179} & \num{0.402}*** & \num{-0.076} \\
& (\num{0.133}) & (\num{0.053}) & (\num{0.262}) \\
Num.Obs. & \num{21} & \num{313} & \num{346} \\
R2 & \num{0.979} & \num{0.476} & \num{0.473} \\
\bottomrule
\end{talltblr}
\end{table} 
\begin{table}
\centering
\begin{talltblr}[         %% tabularray outer open
caption={Treatment Effect on Corruption Measures},
note{}={Significance levels: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.},
]                     %% tabularray outer close
{                     %% tabularray inner open
colspec={Q[]Q[]Q[]Q[]},
column{2-4}={}{halign=c,},
column{1}={}{halign=l,},
hline{4}={1-4}{solid, black, 0.05em},
}                     %% tabularray inner close
\toprule
& Municipal Corruption & Police Corruption & Police Satisfaction \\ \midrule %% TinyTableHeader
Mayoral Reelection & \num{0.256} & \num{0.161}** & \num{-0.076} \\
& (\num{0.228}) & (\num{0.058}) & (\num{0.262}) \\
Num.Obs. & \num{346} & \num{346} & \num{346} \\
R2 & \num{0.335} & \num{0.300} & \num{0.473} \\
\bottomrule
\end{talltblr}
\end{table} 
[1] 1.927011
[1] 1.893478

Call:
   felm(formula = water_safe ~ stag_ind_bin + party_alt + population +      MajorHighway + MajorPort + Airports + Railline + Oilline +      Intlborder + Shoreline + pri_mayor + Pres_misalign + state_misalign |      Bin + State | 0 | State, data = poppies_panel3) 

Residuals:
      Min        1Q    Median        3Q       Max 
-0.130857 -0.000588  0.000000  0.000588  0.130857 

Coefficients:
                 Estimate Cluster s.e. t value Pr(>|t|)  
stag_ind_bin    5.968e-02    2.789e-02   2.139   0.1219  
party_alt      -1.253e-01    1.340e-01  -0.935   0.4187  
population      3.575e-06    1.094e-06   3.267   0.0469 *
MajorHighway          NaN    0.000e+00     NaN      NaN  
MajorPort             NaN    0.000e+00     NaN      NaN  
Airports              NaN    0.000e+00     NaN      NaN  
Railline              NaN    0.000e+00     NaN      NaN  
Oilline        -2.509e+00    5.738e-01  -4.373   0.0221 *
Intlborder     -1.815e+00    6.385e-01  -2.843   0.0655 .
Shoreline             NaN    0.000e+00     NaN      NaN  
pri_mayor       2.001e-01    6.596e-02   3.034   0.0562 .
Pres_misalign  -2.402e-01    8.714e-02  -2.757   0.0704 .
state_misalign  9.204e-02    8.663e-02   1.062   0.3660  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1581 on 3 degrees of freedom
  (885 observations deleted due to missingness)
Multiple R-squared(full model): 0.9497   Adjusted R-squared: 0.6643 
Multiple R-squared(proj model): 0.8239   Adjusted R-squared: -0.1739 
F-statistic(full model, *iid*):3.328 on 17 and 3 DF, p-value: 0.1754 
F-statistic(proj model): 1.418e+05 on 13 and 3 DF, p-value: 2.733e-08 



Call:
   felm(formula = water_safe ~ stag_ind_bin + party_alt + population +      MajorHighway + MajorPort + Airports + Railline + Oilline +      Intlborder + Shoreline + pri_mayor | Bin + State | 0 | State,      data = np_panel3) 

Residuals:
    Min      1Q  Median      3Q     Max 
-1.3288 -0.1867 -0.0428  0.1318  5.8026 

Coefficients:
               Estimate Cluster s.e. t value Pr(>|t|)  
stag_ind_bin  9.625e-02    5.777e-02   1.666   0.1065  
party_alt    -5.015e-02    5.599e-02  -0.896   0.3777  
population   -8.698e-08    1.389e-07  -0.626   0.5361  
MajorHighway  7.043e-02    9.978e-02   0.706   0.4859  
MajorPort    -2.217e-01    1.056e-01  -2.100   0.0446 *
Airports      8.671e-02    7.984e-02   1.086   0.2864  
Railline     -2.430e-01    1.664e-01  -1.460   0.1550  
Oilline       1.849e-01    1.714e-01   1.079   0.2896  
Intlborder   -2.618e-01    1.614e-01  -1.622   0.1155  
Shoreline     5.046e-02    5.619e-02   0.898   0.3765  
pri_mayor    -1.582e-02    7.029e-02  -0.225   0.8235  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.5893 on 335 degrees of freedom
  (6524 observations deleted due to missingness)
Multiple R-squared(full model): 0.185   Adjusted R-squared: 0.0852 
Multiple R-squared(proj model): 0.01752   Adjusted R-squared: -0.1027 
F-statistic(full model, *iid*):1.854 on 41 and 335 DF, p-value: 0.001792 
F-statistic(proj model): 4.665 on 11 and 29 DF, p-value: 0.0004215 



Call:
   felm(formula = water_safe ~ stag_ind_bin + MajorHighway + population +      MajorPort + Airports + Railline + Oilline + Intlborder +      Shoreline + pri_mayor + Pres_misalign + state_misalign |      Bin + State | 0 | State, data = test_merge) 

Residuals:
    Min      1Q  Median      3Q     Max 
-1.4814 -0.1877 -0.0272  0.1493  5.8311 

Coefficients:
                 Estimate Cluster s.e. t value Pr(>|t|)  
stag_ind_bin   -3.163e-01    2.685e-01  -1.178   0.2482  
MajorHighway    7.447e-02    8.718e-02   0.854   0.3998  
population     -1.472e-07    1.459e-07  -1.009   0.3211  
MajorPort      -2.464e-01    9.407e-02  -2.620   0.0137 *
Airports        8.770e-02    7.981e-02   1.099   0.2806  
Railline       -2.612e-01    1.314e-01  -1.987   0.0561 .
Oilline         1.984e-01    1.410e-01   1.407   0.1697  
Intlborder     -2.288e-01    1.405e-01  -1.628   0.1140  
Shoreline       5.901e-02    8.123e-02   0.726   0.4732  
pri_mayor      -9.401e-02    4.607e-02  -2.041   0.0502 .
Pres_misalign   2.434e-01    1.391e-01   1.750   0.0904 .
state_misalign -2.349e-02    7.683e-02  -0.306   0.7619  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.5772 on 302 degrees of freedom
  (7461 observations deleted due to missingness)
Multiple R-squared(full model): 0.2521   Adjusted R-squared: 0.1456 
Multiple R-squared(proj model): 0.03159   Adjusted R-squared: -0.1063 
F-statistic(full model, *iid*):2.367 on 43 and 302 DF, p-value: 1.336e-05 
F-statistic(proj model): 4.118 on 12 and 30 DF, p-value: 0.0008034 


\begin{table}
\centering
\begin{talltblr}[         %% tabularray outer open
caption={Treatment Effect on Corruption Measures in Criminally-Entrenched Areas and non-Entrenched Areas},
note{}={Significance levels: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.},
]                     %% tabularray outer close
{                     %% tabularray inner open
colspec={Q[]Q[]Q[]Q[]},
column{2-4}={}{halign=c,},
column{1}={}{halign=l,},
hline{4}={1-4}{solid, black, 0.05em},
}                     %% tabularray inner close
\toprule
& CE & Non CE & National \\ \midrule %% TinyTableHeader
Mayoral Reelection & \num{0.060} & \num{0.096} & \num{-0.316} \\
& (\num{0.028}) & (\num{0.058}) & (\num{0.269}) \\
Num.Obs. & \num{21} & \num{377} & \num{346} \\
R2 & \num{0.950} & \num{0.185} & \num{0.252} \\
\bottomrule
\end{talltblr}
\end{table} 
[1] 1.65629
[1] 1.599348

Call:
   felm(formula = street_lighting ~ stag_ind_bin + party_alt + population +      MajorHighway + MajorPort + Airports + Railline + Oilline +      Intlborder + Shoreline + pri_mayor + Pres_misalign + state_misalign |      Bin + State | 0 | State, data = poppies_panel3) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.02055 -0.01027  0.00000  0.01027  0.02055 

Coefficients:
                 Estimate Cluster s.e. t value Pr(>|t|)   
stag_ind_bin   -1.329e-01    2.708e-02  -4.907  0.01620 * 
party_alt      -5.607e-02    4.281e-02  -1.310  0.28157   
population      2.156e-06    2.867e-07   7.521  0.00487 **
MajorHighway          NaN    0.000e+00     NaN      NaN   
MajorPort             NaN    0.000e+00     NaN      NaN   
Airports              NaN    0.000e+00     NaN      NaN   
Railline              NaN    0.000e+00     NaN      NaN   
Oilline        -1.384e+00    1.599e-01  -8.656  0.00324 **
Intlborder     -9.043e-01    1.691e-01  -5.349  0.01278 * 
Shoreline             NaN    0.000e+00     NaN      NaN   
pri_mayor       7.102e-02    2.806e-02   2.531  0.08532 . 
Pres_misalign  -1.883e-01    5.928e-02  -3.177  0.05022 . 
state_misalign  1.464e-01    4.098e-02   3.572  0.03750 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.03292 on 3 degrees of freedom
  (885 observations deleted due to missingness)
Multiple R-squared(full model): 0.9913   Adjusted R-squared: 0.9423 
Multiple R-squared(proj model): 0.9798   Adjusted R-squared: 0.8655 
F-statistic(full model, *iid*):20.21 on 17 and 3 DF, p-value: 0.01507 
F-statistic(proj model):   412 on 13 and 3 DF, p-value: 0.0001741 



Call:
   felm(formula = street_lighting ~ stag_ind_bin + party_alt + population +      MajorHighway + MajorPort + Airports + Railline + Oilline +      Intlborder + Shoreline + pri_mayor | Bin + State | 0 | State,      data = np_panel3) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.57040 -0.09778  0.00188  0.09824  1.16604 

Coefficients:
               Estimate Cluster s.e. t value Pr(>|t|)  
stag_ind_bin  4.332e-02    1.597e-02   2.713   0.0111 *
party_alt    -7.981e-03    1.756e-02  -0.455   0.6528  
population    4.906e-09    2.343e-08   0.209   0.8356  
MajorHighway -4.577e-02    3.419e-02  -1.339   0.1911  
MajorPort    -7.087e-02    2.977e-02  -2.380   0.0241 *
Airports     -2.544e-02    1.923e-02  -1.323   0.1961  
Railline     -5.133e-02    6.952e-02  -0.738   0.4662  
Oilline       6.601e-02    7.240e-02   0.912   0.3694  
Intlborder    2.956e-02    2.972e-02   0.995   0.3281  
Shoreline     9.984e-02    3.680e-02   2.713   0.0111 *
pri_mayor     5.081e-02    4.004e-02   1.269   0.2145  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1851 on 335 degrees of freedom
  (6524 observations deleted due to missingness)
Multiple R-squared(full model): 0.2049   Adjusted R-squared: 0.1076 
Multiple R-squared(proj model): 0.02814   Adjusted R-squared: -0.09081 
F-statistic(full model, *iid*):2.106 on 41 and 335 DF, p-value: 0.0001876 
F-statistic(proj model): 5.902 on 11 and 29 DF, p-value: 5.988e-05 



Call:
   felm(formula = street_lighting ~ stag_ind_bin + MajorHighway +      population + MajorPort + Airports + Railline + Oilline +      Intlborder + Shoreline + pri_mayor + Pres_misalign + state_misalign |      Bin + State | 0 | State, data = test_merge) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.55941 -0.08856 -0.00108  0.09821  1.14946 

Coefficients:
                 Estimate Cluster s.e. t value Pr(>|t|)
stag_ind_bin   -4.535e-02    4.444e-02  -1.020    0.316
MajorHighway   -4.350e-02    5.614e-02  -0.775    0.444
population     -5.842e-10    2.395e-08  -0.024    0.981
MajorPort      -1.735e-02    5.596e-02  -0.310    0.759
Airports       -2.646e-02    1.945e-02  -1.361    0.184
Railline       -3.534e-02    5.388e-02  -0.656    0.517
Oilline         5.338e-02    5.712e-02   0.934    0.358
Intlborder      4.935e-02    3.291e-02   1.500    0.144
Shoreline       4.608e-02    3.938e-02   1.170    0.251
pri_mayor       6.671e-02    7.136e-02   0.935    0.357
Pres_misalign  -1.163e-02    7.098e-02  -0.164    0.871
state_misalign -1.047e-02    2.854e-02  -0.367    0.716

Residual standard error: 0.1804 on 302 degrees of freedom
  (7461 observations deleted due to missingness)
Multiple R-squared(full model): 0.2331   Adjusted R-squared: 0.1239 
Multiple R-squared(proj model): 0.0318   Adjusted R-squared: -0.1061 
F-statistic(full model, *iid*):2.135 on 43 and 302 DF, p-value: 0.0001264 
F-statistic(proj model): 2.942 on 12 and 30 DF, p-value: 0.008142 


\begin{table}
\centering
\begin{talltblr}[         %% tabularray outer open
caption={Treatment Effect on Corruption Measures in Criminally-Entrenched Areas and non-Entrenched Areas},
note{}={Significance levels: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.},
]                     %% tabularray outer close
{                     %% tabularray inner open
colspec={Q[]Q[]Q[]Q[]},
column{2-4}={}{halign=c,},
column{1}={}{halign=l,},
hline{4}={1-4}{solid, black, 0.05em},
}                     %% tabularray inner close
\toprule
& CE & Non CE & National \\ \midrule %% TinyTableHeader
Mayoral Reelection & \num{-0.133}* & \num{0.043}* & \num{-0.045} \\
& (\num{0.027}) & (\num{0.016}) & (\num{0.044}) \\
Num.Obs. & \num{21} & \num{377} & \num{346} \\
R2 & \num{0.991} & \num{0.205} & \num{0.233} \\
\bottomrule
\end{talltblr}
\end{table} 
[1] 1.398835
[1] 1.43942

Call:
   felm(formula = trash_collection ~ stag_ind_bin + party_alt +      population + MajorHighway + MajorPort + Airports + Railline +      Oilline + Intlborder + Shoreline + pri_mayor + Pres_misalign +      state_misalign | Bin + State | 0 | State, data = poppies_panel3) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.41918 -0.07162  0.00000  0.07162  0.41918 

Coefficients:
                 Estimate Cluster s.e. t value Pr(>|t|)
stag_ind_bin   -1.760e-02    1.110e-01  -0.159    0.884
party_alt      -2.838e-01    3.536e-01  -0.803    0.481
population      9.210e-07    3.074e-06   0.300    0.784
MajorHighway          NaN    0.000e+00     NaN      NaN
MajorPort             NaN    0.000e+00     NaN      NaN
Airports              NaN    0.000e+00     NaN      NaN
Railline              NaN    0.000e+00     NaN      NaN
Oilline        -9.783e-01    1.640e+00  -0.596    0.593
Intlborder     -2.663e-01    1.780e+00  -0.150    0.891
Shoreline             NaN    0.000e+00     NaN      NaN
pri_mayor       1.359e-01    1.751e-01   0.776    0.494
Pres_misalign  -4.258e-01    2.868e-01  -1.485    0.234
state_misalign  2.539e-01    2.969e-01   0.855    0.455

Residual standard error: 0.462 on 3 degrees of freedom
  (885 observations deleted due to missingness)
Multiple R-squared(full model): 0.6407   Adjusted R-squared: -1.395 
Multiple R-squared(proj model): 0.2648   Adjusted R-squared: -3.901 
F-statistic(full model, *iid*):0.3147 on 17 and 3 DF, p-value: 0.9492 
F-statistic(proj model): 4.022 on 13 and 3 DF, p-value: 0.1391 



Call:
   felm(formula = trash_collection ~ stag_ind_bin + party_alt +      population + MajorHighway + MajorPort + Airports + Railline +      Oilline + Intlborder + Shoreline + pri_mayor | Bin + State |      0 | State, data = np_panel3) 

Residuals:
    Min      1Q  Median      3Q     Max 
-1.6271 -0.1911 -0.0402  0.1175  6.2063 

Coefficients:
               Estimate Cluster s.e. t value Pr(>|t|)
stag_ind_bin  3.600e-02    1.091e-01   0.330    0.744
party_alt    -1.031e-02    3.158e-02  -0.327    0.746
population   -6.671e-08    6.601e-08  -1.011    0.321
MajorHighway -2.342e-02    5.298e-02  -0.442    0.662
MajorPort     4.232e-02    6.573e-02   0.644    0.525
Airports     -1.638e-01    1.175e-01  -1.394    0.174
Railline      3.220e-02    6.297e-02   0.511    0.613
Oilline       6.729e-02    9.023e-02   0.746    0.462
Intlborder    4.450e-02    6.819e-02   0.653    0.519
Shoreline    -9.461e-02    6.955e-02  -1.360    0.184
pri_mayor     2.163e-02    1.502e-01   0.144    0.886

Residual standard error: 0.5412 on 335 degrees of freedom
  (6524 observations deleted due to missingness)
Multiple R-squared(full model): 0.2389   Adjusted R-squared: 0.1458 
Multiple R-squared(proj model): 0.02013   Adjusted R-squared: -0.09979 
F-statistic(full model, *iid*):2.565 on 41 and 335 DF, p-value: 2.189e-06 
F-statistic(proj model):  1.82 on 11 and 29 DF, p-value: 0.09643 



Call:
   felm(formula = trash_collection ~ stag_ind_bin + MajorHighway +      population + MajorPort + Airports + Railline + Oilline +      Intlborder + Shoreline + pri_mayor + Pres_misalign + state_misalign |      Bin + State | 0 | State, data = test_merge) 

Residuals:
    Min      1Q  Median      3Q     Max 
-1.6104 -0.1996 -0.0359  0.1105  6.1547 

Coefficients:
                 Estimate Cluster s.e. t value Pr(>|t|)
stag_ind_bin   -1.134e-01    1.358e-01  -0.835    0.410
MajorHighway   -4.311e-03    4.937e-02  -0.087    0.931
population     -7.021e-08    7.065e-08  -0.994    0.328
MajorPort       6.368e-02    7.597e-02   0.838    0.408
Airports       -1.753e-01    1.184e-01  -1.480    0.149
Railline        5.954e-02    8.695e-02   0.685    0.499
Oilline         4.387e-02    1.278e-01   0.343    0.734
Intlborder      4.397e-02    4.803e-02   0.915    0.367
Shoreline      -9.853e-02    1.014e-01  -0.972    0.339
pri_mayor       8.582e-02    2.674e-01   0.321    0.750
Pres_misalign  -6.831e-02    2.260e-01  -0.302    0.765
state_misalign -1.078e-01    1.595e-01  -0.676    0.504

Residual standard error: 0.5609 on 302 degrees of freedom
  (7461 observations deleted due to missingness)
Multiple R-squared(full model): 0.2485   Adjusted R-squared: 0.1415 
Multiple R-squared(proj model): 0.02601   Adjusted R-squared: -0.1127 
F-statistic(full model, *iid*):2.322 on 43 and 302 DF, p-value: 2.087e-05 
F-statistic(proj model): 0.9915 on 12 and 30 DF, p-value: 0.4792 


\begin{table}
\centering
\begin{talltblr}[         %% tabularray outer open
caption={Treatment Effect on Corruption Measures in Criminally-Entrenched Areas and non-Entrenched Areas},
note{}={Significance levels: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.},
]                     %% tabularray outer close
{                     %% tabularray inner open
colspec={Q[]Q[]Q[]Q[]},
column{2-4}={}{halign=c,},
column{1}={}{halign=l,},
hline{4}={1-4}{solid, black, 0.05em},
}                     %% tabularray inner close
\toprule
& CE & Non CE & National \\ \midrule %% TinyTableHeader
Mayoral Reelection & \num{-0.018} & \num{0.036} & \num{-0.113} \\
& (\num{0.111}) & (\num{0.109}) & (\num{0.136}) \\
Num.Obs. & \num{21} & \num{377} & \num{346} \\
R2 & \num{0.641} & \num{0.239} & \num{0.248} \\
\bottomrule
\end{talltblr}
\end{table} 
\begin{table}
\centering
\begin{talltblr}[         %% tabularray outer open
caption={Treatment Effect on Governance Quality},
note{}={Significance levels: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.},
]                     %% tabularray outer close
{                     %% tabularray inner open
colspec={Q[]Q[]Q[]Q[]},
column{2-4}={}{halign=c,},
column{1}={}{halign=l,},
hline{4}={1-4}{solid, black, 0.05em},
}                     %% tabularray inner close
\toprule
& Water Drinkability & Street Lighting & Trash Collection \\ \midrule %% TinyTableHeader
Mayoral Reelection & \num{-0.316} & \num{-0.045} & \num{-0.113} \\
& (\num{0.269}) & (\num{0.044}) & (\num{0.136}) \\
Num.Obs. & \num{346} & \num{346} & \num{346} \\
R2 & \num{0.252} & \num{0.233} & \num{0.248} \\
\bottomrule
\end{talltblr}
\end{table} 

Call:
   felm(formula = Bin_inc ~ stag_ind_bin * Poppies + Pres_misalign +      population + MajorHighway + MajorPort + Airports + Railline +      Oilline + Intlborder + Shoreline + pri_mayor + state_misalign +      party_alt | Bin + State | 0 | State, data = panel) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.06004 -0.01565 -0.00768 -0.00101  1.96437 

Coefficients:
                       Estimate Cluster s.e. t value Pr(>|t|)  
stag_ind_bin         -7.687e-04    4.050e-03  -0.190   0.8508  
Poppies               2.152e-02    1.197e-02   1.797   0.0823 .
Pres_misalign         1.270e-04    6.768e-03   0.019   0.9852  
population            1.157e-08    1.590e-08   0.728   0.4722  
MajorHighway          1.551e-03    2.985e-03   0.520   0.6072  
MajorPort             1.120e-02    1.891e-02   0.592   0.5582  
Airports              2.105e-03    5.654e-03   0.372   0.7123  
Railline              2.923e-03    5.914e-03   0.494   0.6247  
Oilline              -5.225e-03    6.072e-03  -0.861   0.3963  
Intlborder            2.317e-03    1.085e-02   0.214   0.8323  
Shoreline             5.538e-04    4.548e-03   0.122   0.9039  
pri_mayor            -1.435e-04    3.149e-03  -0.046   0.9640  
state_misalign        5.695e-03    4.223e-03   1.348   0.1876  
party_alt             2.998e-03    2.030e-03   1.476   0.1503  
stag_ind_bin:Poppies -4.397e-03    1.566e-02  -0.281   0.7808  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.09986 on 5889 degrees of freedom
  (1869 observations deleted due to missingness)
Multiple R-squared(full model): 0.01263   Adjusted R-squared: 0.004587 
Multiple R-squared(proj model): 0.003729   Adjusted R-squared: -0.004391 
F-statistic(full model, *iid*): 1.57 on 48 and 5889 DF, p-value: 0.007339 
F-statistic(proj model):  6.18 on 15 and 30 DF, p-value: 1.203e-05 



Call:
   felm(formula = Bin_chal ~ stag_ind_bin * Poppies + Pres_misalign +      population + MajorHighway + MajorPort + Airports + Railline +      Oilline + Intlborder + Shoreline + pri_mayor + state_misalign +      party_alt | Bin + State | 0 | State, data = panel) 

Residuals:
    Min      1Q  Median      3Q     Max 
-0.3333 -0.0670 -0.0239  0.0025  6.7545 

Coefficients:
                       Estimate Cluster s.e. t value Pr(>|t|)   
stag_ind_bin         -3.995e-02    1.357e-02  -2.944   0.0062 **
Poppies               1.325e-02    1.362e-02   0.973   0.3383   
Pres_misalign         2.452e-02    1.331e-02   1.843   0.0753 . 
population            1.188e-07    5.023e-08   2.364   0.0247 * 
MajorHighway          1.357e-02    6.083e-03   2.231   0.0333 * 
MajorPort            -8.355e-03    7.155e-02  -0.117   0.9078   
Airports              1.422e-02    1.061e-02   1.340   0.1903   
Railline             -2.246e-02    2.491e-02  -0.902   0.3744   
Oilline               2.150e-02    2.462e-02   0.873   0.3893   
Intlborder           -1.846e-03    2.190e-02  -0.084   0.9334   
Shoreline             9.786e-03    1.507e-02   0.649   0.5210   
pri_mayor            -2.417e-02    8.940e-03  -2.704   0.0112 * 
state_misalign        3.245e-03    8.466e-03   0.383   0.7042   
party_alt             5.270e-03    4.797e-03   1.099   0.2806   
stag_ind_bin:Poppies  3.406e-02    2.410e-02   1.413   0.1679   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2336 on 5889 degrees of freedom
  (1869 observations deleted due to missingness)
Multiple R-squared(full model): 0.05296   Adjusted R-squared: 0.04524 
Multiple R-squared(proj model): 0.01286   Adjusted R-squared: 0.004815 
F-statistic(full model, *iid*): 6.86 on 48 and 5889 DF, p-value: < 2.2e-16 
F-statistic(proj model): 4.422 on 15 and 30 DF, p-value: 0.0002627 



Call:
   felm(formula = Bin_attacks2 ~ stag_ind_bin * Poppies + Pres_misalign +      population + MajorHighway + MajorPort + Airports + Railline +      Oilline + Intlborder + Shoreline + pri_mayor + state_misalign +      party_alt | Bin + State | 0 | State, data = panel) 

Residuals:
    Min      1Q  Median      3Q     Max 
-0.3829 -0.0791 -0.0309  0.0016  6.7112 

Coefficients:
                       Estimate Cluster s.e. t value Pr(>|t|)   
stag_ind_bin         -4.071e-02    1.295e-02  -3.143  0.00375 **
Poppies               3.477e-02    1.639e-02   2.122  0.04222 * 
Pres_misalign         2.465e-02    1.590e-02   1.551  0.13146   
population            1.303e-07    5.498e-08   2.370  0.02439 * 
MajorHighway          1.512e-02    5.484e-03   2.758  0.00981 **
MajorPort             2.846e-03    7.142e-02   0.040  0.96848   
Airports              1.632e-02    1.231e-02   1.326  0.19498   
Railline             -1.954e-02    3.011e-02  -0.649  0.52140   
Oilline               1.628e-02    3.004e-02   0.542  0.59191   
Intlborder            4.708e-04    3.098e-02   0.015  0.98798   
Shoreline             1.034e-02    1.611e-02   0.642  0.52594   
pri_mayor            -2.431e-02    8.595e-03  -2.829  0.00825 **
state_misalign        8.940e-03    9.566e-03   0.935  0.35745   
party_alt             8.268e-03    5.545e-03   1.491  0.14642   
stag_ind_bin:Poppies  2.967e-02    3.177e-02   0.934  0.35792   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2595 on 5889 degrees of freedom
  (1869 observations deleted due to missingness)
Multiple R-squared(full model): 0.05634   Adjusted R-squared: 0.04865 
Multiple R-squared(proj model): 0.014   Adjusted R-squared: 0.00596 
F-statistic(full model, *iid*):7.325 on 48 and 5889 DF, p-value: < 2.2e-16 
F-statistic(proj model):  5.93 on 15 and 30 DF, p-value: 1.805e-05 


\begin{table}
\centering
\begin{talltblr}[         %% tabularray outer open
caption={Treatment Effect on Attacks Against Politicians},
note{}={Significance levels: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.},
]                     %% tabularray outer close
{                     %% tabularray inner open
colspec={Q[]Q[]Q[]Q[]},
column{2-4}={}{halign=c,},
column{1}={}{halign=l,},
hline{8}={1-4}{solid, black, 0.05em},
}                     %% tabularray inner close
\toprule
& Total Attacks & Incumbents & Challengers \\ \midrule %% TinyTableHeader
Mayoral Reelection & \num{-0.041}** & \num{-0.001} & \num{-0.040}** \\
& (\num{0.013}) & (\num{0.004}) & (\num{0.014}) \\
Criminal Presence & \num{0.035}* & \num{0.022}+ & \num{0.013} \\
& (\num{0.016}) & (\num{0.012}) & (\num{0.014}) \\
Mayoral Reelection × Criminal Presence & \num{0.030} & \num{-0.004} & \num{0.034} \\
& (\num{0.032}) & (\num{0.016}) & (\num{0.024}) \\
Stacked Obs. & \num{\num{5938}} & \num{\num{5938}} & \num{\num{5938}} \\
R-squared & \num{0.056} & \num{0.013} & \num{0.053} \\
Unique Obs. & 2011 & 2011 & 2011 \\
\bottomrule
\end{talltblr}
\end{table} 
Diff: Criminal - Not & \num{0.070}+ & & \\
 & (\num{0.037}) \\
Diff: Criminal - Not & & \num{-0.004} & \\
 & (\num{0.018}) \\
Diff: Criminal - Not & \num{0.074}* \\
 & (\num{0.030}) \\
Unique Obs. & 2011 & 2011 & 2011 \\
[1] 0.04636864
[1] 0.03970439
[1] 0.09713024
[1] 22396    44
[1] 2387   44

Call:
   felm(formula = Bin_attacks2 ~ stag_ind_bin + Pres_misalign +      population + MajorHighway + MajorPort + Airports + Railline +      Oilline + Intlborder + Shoreline + pri_mayor + state_misalign +      Bin:Year + Bin:State | Bin + State | 0 | State, data = df_alt2) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.50416 -0.14084 -0.02266  0.04257  1.72811 

Coefficients:
                      Estimate Cluster s.e.  t value Pr(>|t|)    
stag_ind_bin         5.809e-01    2.913e-01    1.994   0.0773 .  
Pres_misalign       -2.535e-02    7.639e-02   -0.332   0.7476    
population           7.593e-07    3.775e-07    2.011   0.0751 .  
MajorHighway        -8.232e-03    3.636e-02   -0.226   0.8259    
MajorPort                  NaN    0.000e+00      NaN      NaN    
Airports            -1.431e-02    2.721e-02   -0.526   0.6115    
Railline             5.007e-02    6.843e-02    0.732   0.4830    
Oilline              4.902e-02    9.926e-02    0.494   0.6333    
Intlborder           1.486e-01    1.917e-01    0.775   0.4580    
Shoreline           -4.622e-02    2.926e-02   -1.580   0.1487    
pri_mayor           -5.508e-02    5.108e-02   -1.078   0.3090    
state_misalign       1.259e-01    4.931e-02    2.554   0.0310 *  
Bin:Year                   NaN    0.000e+00      NaN      NaN    
Bin:StateChiapas    -2.866e-01    1.232e-02  -23.259 2.39e-09 ***
Bin:StateChihuahua  -2.892e-01    4.286e-03  -67.473 1.74e-13 ***
Bin:StateDurango    -4.008e-01    3.572e-02  -11.221 1.36e-06 ***
Bin:StateNayarit    -8.110e-02    1.179e-01   -0.688   0.5090    
Bin:StateOaxaca     -2.324e-01    4.768e-03  -48.733 3.23e-12 ***
Bin:StatePuebla            NaN    0.000e+00      NaN      NaN    
Bin:StateSinaloa    -3.123e-01    1.274e-02  -24.522 1.49e-09 ***
Bin:StateTamaulipas -3.221e-01    2.295e-02  -14.034 2.01e-07 ***
Bin:StateZacatecas  -2.959e-01    2.028e-03 -145.953  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.342 on 170 degrees of freedom
  (12 observations deleted due to missingness)
Multiple R-squared(full model): 0.2116   Adjusted R-squared: 0.06779 
Multiple R-squared(proj model): 0.08075   Adjusted R-squared: -0.08687 
F-statistic(full model, *iid*):1.471 on 31 and 170 DF, p-value: 0.0643 
F-statistic(proj model): 2.132 on 22 and 9 DF, p-value: 0.1202 



Call:
   felm(formula = Bin_attacks2 ~ stag_ind_bin + Pres_misalign +      population + MajorHighway + MajorPort + Airports + Railline +      Oilline + Intlborder + Shoreline + pri_mayor + state_misalign +      Bin:Year + Bin:State | Bin + State | 0 | State, data = df_no_poppies) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.47041 -0.05287 -0.02184 -0.00047  1.92222 

Coefficients:
                           Estimate Cluster s.e. t value Pr(>|t|)    
stag_ind_bin              2.090e-02    9.306e-03   2.245 0.041410 *  
Pres_misalign             3.567e-03    7.010e-03   0.509 0.618794    
population                1.777e-07    1.004e-07   1.770 0.098539 .  
MajorHighway              1.482e-02    1.207e-02   1.228 0.239669    
MajorPort                -9.459e-02    2.376e-02  -3.981 0.001366 ** 
Airports                  5.272e-02    3.217e-02   1.639 0.123542    
Railline                 -1.299e-02    1.469e-02  -0.884 0.391470    
Oilline                  -6.154e-03    1.628e-02  -0.378 0.711106    
Intlborder               -5.092e-04    2.330e-02  -0.022 0.982876    
Shoreline                -1.294e-02    1.444e-02  -0.896 0.385468    
pri_mayor                -1.326e-02    3.351e-03  -3.957 0.001433 ** 
state_misalign           -3.305e-03    1.044e-02  -0.317 0.756191    
Bin:Year                        NaN    0.000e+00     NaN      NaN    
Bin:StateBaja California -1.729e-03    7.962e-04  -2.172 0.047566 *  
Bin:StateChiapas          2.593e-03    2.404e-03   1.079 0.299003    
Bin:StateChihuahua        2.264e-02    2.452e-03   9.233 2.49e-07 ***
Bin:StateDurango         -4.654e-02    7.036e-03  -6.615 1.16e-05 ***
Bin:StateNayarit          1.421e-02    2.095e-03   6.783 8.84e-06 ***
Bin:StateOaxaca          -3.099e-03    7.187e-04  -4.312 0.000716 ***
Bin:StatePuebla           1.121e-02    1.436e-03   7.807 1.82e-06 ***
Bin:StateQuintana Roo     1.072e-01    6.480e-03  16.547 1.38e-10 ***
Bin:StateSinaloa          3.475e-03    3.231e-03   1.076 0.300239    
Bin:StateTamaulipas      -2.424e-02    2.577e-03  -9.405 1.99e-07 ***
Bin:StateTlaxcala         8.517e-03    2.626e-03   3.244 0.005883 ** 
Bin:StateVeracruz         1.774e-02    2.949e-03   6.014 3.18e-05 ***
Bin:StateYucatan         -7.813e-04    8.030e-04  -0.973 0.347070    
Bin:StateZacatecas       -2.173e-03    2.664e-03  -0.816 0.428372    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1846 on 1987 degrees of freedom
  (194 observations deleted due to missingness)
Multiple R-squared(full model): 0.05795   Adjusted R-squared: 0.03756 
Multiple R-squared(proj model): 0.02594   Adjusted R-squared: 0.004857 
F-statistic(full model, *iid*):2.843 on 43 and 1987 DF, p-value: 3.548e-09 
F-statistic(proj model): 12.08 on 27 and 14 DF, p-value: 7.937e-06 



Call:
   felm(formula = Bin_inc ~ stag_ind_bin + Pres_misalign + population +      MajorHighway + MajorPort + Airports + Railline + Oilline +      Intlborder + Shoreline + pri_mayor + state_misalign + Bin:Year +      Bin:State | Bin + State | 0 | State, data = df_no_poppies) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.15118 -0.01401 -0.00568  0.00101  0.99976 

Coefficients:
                           Estimate Cluster s.e. t value Pr(>|t|)    
stag_ind_bin              7.723e-03    5.738e-03   1.346  0.19969    
Pres_misalign             4.242e-03    7.780e-03   0.545  0.59418    
population                8.549e-08    4.922e-08   1.737  0.10436    
MajorHighway              2.710e-03    1.329e-03   2.039  0.06077 .  
MajorPort                -1.665e-02    1.374e-02  -1.212  0.24571    
Airports                 -1.826e-02    6.751e-03  -2.705  0.01708 *  
Railline                  1.275e-02    6.058e-03   2.104  0.05389 .  
Oilline                  -6.724e-03    6.823e-03  -0.985  0.34112    
Intlborder               -5.756e-03    2.837e-03  -2.029  0.06194 .  
Shoreline                 5.007e-03    5.386e-03   0.930  0.36830    
pri_mayor                -6.806e-03    7.457e-03  -0.913  0.37690    
state_misalign           -2.135e-03    2.040e-03  -1.046  0.31311    
Bin:Year                        NaN    0.000e+00     NaN      NaN    
Bin:StateBaja California -9.095e-04    1.129e-03  -0.806  0.43380    
Bin:StateChiapas          3.574e-03    6.101e-04   5.859 4.15e-05 ***
Bin:StateChihuahua        1.486e-03    7.811e-04   1.902  0.07789 .  
Bin:StateDurango         -3.243e-02    3.112e-03 -10.423 5.58e-08 ***
Bin:StateNayarit          1.688e-04    3.237e-03   0.052  0.95915    
Bin:StateOaxaca          -8.747e-03    5.832e-04 -14.999 5.10e-10 ***
Bin:StatePuebla          -7.977e-04    7.012e-04  -1.138  0.27435    
Bin:StateQuintana Roo    -7.262e-03    4.375e-03  -1.660  0.11917    
Bin:StateSinaloa         -3.920e-03    2.514e-03  -1.560  0.14118    
Bin:StateTamaulipas      -7.813e-04    1.169e-03  -0.669  0.51466    
Bin:StateTlaxcala         2.566e-03    2.037e-03   1.259  0.22854    
Bin:StateVeracruz        -7.324e-03    2.266e-03  -3.232  0.00603 ** 
Bin:StateYucatan         -2.821e-04    2.430e-04  -1.161  0.26522    
Bin:StateZacatecas       -6.551e-04    8.147e-04  -0.804  0.43479    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.08827 on 1987 degrees of freedom
  (194 observations deleted due to missingness)
Multiple R-squared(full model): 0.02468   Adjusted R-squared: 0.003573 
Multiple R-squared(proj model): 0.01706   Adjusted R-squared: -0.004214 
F-statistic(full model, *iid*):1.169 on 43 and 1987 DF, p-value: 0.2106 
F-statistic(proj model): 71.25 on 27 and 14 DF, p-value: 5.952e-11 



Call:
   felm(formula = Bin_chal ~ stag_ind_bin + Pres_misalign + population +      MajorHighway + MajorPort + Airports + Railline + Oilline +      Intlborder + Shoreline + pri_mayor + state_misalign + Bin:Year +      Bin:State | Bin + State | 0 | State, data = df_no_poppies) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.43551 -0.04074 -0.01239  0.00281  1.93394 

Coefficients:
                           Estimate Cluster s.e. t value Pr(>|t|)    
stag_ind_bin              1.317e-02    9.945e-03   1.325 0.206495    
Pres_misalign            -6.752e-04    9.602e-03  -0.070 0.944931    
population                9.219e-08    5.475e-08   1.684 0.114409    
MajorHighway              1.211e-02    1.239e-02   0.978 0.344795    
MajorPort                -7.794e-02    2.139e-02  -3.644 0.002656 ** 
Airports                  7.099e-02    3.037e-02   2.338 0.034775 *  
Railline                 -2.574e-02    1.294e-02  -1.989 0.066578 .  
Oilline                   5.694e-04    1.124e-02   0.051 0.960331    
Intlborder                5.247e-03    2.425e-02   0.216 0.831802    
Shoreline                -1.794e-02    1.837e-02  -0.977 0.345172    
pri_mayor                -6.451e-03    7.402e-03  -0.872 0.398156    
state_misalign           -1.170e-03    1.013e-02  -0.116 0.909633    
Bin:Year                        NaN    0.000e+00     NaN      NaN    
Bin:StateBaja California -8.194e-04    1.348e-03  -0.608 0.553107    
Bin:StateChiapas         -9.817e-04    2.250e-03  -0.436 0.669261    
Bin:StateChihuahua        2.115e-02    1.855e-03  11.405 1.79e-08 ***
Bin:StateDurango         -1.411e-02    5.886e-03  -2.397 0.031023 *  
Bin:StateNayarit          1.404e-02    2.677e-03   5.244 0.000124 ***
Bin:StateOaxaca           5.648e-03    6.903e-04   8.181 1.05e-06 ***
Bin:StatePuebla           1.201e-02    8.156e-04  14.727 6.49e-10 ***
Bin:StateQuintana Roo     1.145e-01    2.947e-03  38.846 1.17e-15 ***
Bin:StateSinaloa          7.396e-03    1.265e-03   5.848 4.24e-05 ***
Bin:StateTamaulipas      -2.345e-02    2.796e-03  -8.388 7.86e-07 ***
Bin:StateTlaxcala         5.952e-03    3.016e-03   1.973 0.068537 .  
Bin:StateVeracruz         2.506e-02    3.671e-03   6.826 8.24e-06 ***
Bin:StateYucatan         -4.993e-04    7.251e-04  -0.689 0.502361    
Bin:StateZacatecas       -1.518e-03    2.434e-03  -0.624 0.542843    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1608 on 1987 degrees of freedom
  (194 observations deleted due to missingness)
Multiple R-squared(full model): 0.06216   Adjusted R-squared: 0.04187 
Multiple R-squared(proj model): 0.0263   Adjusted R-squared: 0.005233 
F-statistic(full model, *iid*):3.063 on 43 and 1987 DF, p-value: 1.683e-10 
F-statistic(proj model): 17.92 on 27 and 14 DF, p-value: 6.39e-07 


\begin{table}
\centering
\begin{talltblr}[         %% tabularray outer open
caption={Treatment Effect on Attacks (Poppies and Party Alternation (2012) Subset)},
note{}={Significance levels: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.},
]                     %% tabularray outer close
{                     %% tabularray inner open
colspec={Q[]Q[]Q[]Q[]Q[]Q[]Q[]},
column{2-7}={}{halign=c,},
column{1}={}{halign=l,},
hline{4}={1-7}{solid, black, 0.05em},
}                     %% tabularray inner close
\toprule
& Baseline & Incumbent & Challenger & No Poppies & No Poppy Incumbent & No Poppy Challenger \\ \midrule %% TinyTableHeader
Mayoral Reelection & \num{0.581}+ & \num{0.160} & \num{0.421}** & \num{0.021}* & \num{0.008} & \num{0.013} \\
& (\num{0.291}) & (\num{0.179}) & (\num{0.126}) & (\num{0.009}) & (\num{0.006}) & (\num{0.010}) \\
Num.Obs. & \num{202} & \num{202} & \num{202} & \num{2031} & \num{2031} & \num{2031} \\
R2 & \num{0.212} & \num{0.104} & \num{0.184} & \num{0.058} & \num{0.025} & \num{0.062} \\
\bottomrule
\end{talltblr}
\end{table} 

Call:
   felm(formula = if_audit ~ stag_ind_bin + Pres_misalign + population +      MajorHighway + MajorPort + Airports + Railline + Oilline +      Intlborder + Shoreline + pri_mayor + state_misalign + Bin:Year +      Bin:State | Bin + State | 0 | State, data = df_alt3) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.55216 -0.06363 -0.01329  0.00573  1.00490 

Coefficients:
                           Estimate Cluster s.e.  t value Pr(>|t|)    
stag_ind_bin              5.714e-03    5.459e-02    0.105 0.918123    
Pres_misalign            -3.324e-02    5.772e-02   -0.576 0.573780    
population                2.182e-07    7.988e-08    2.732 0.016202 *  
MajorHighway             -8.473e-04    1.672e-02   -0.051 0.960303    
MajorPort                 1.071e-01    7.329e-02    1.462 0.165919    
Airports                  1.580e-02    4.545e-02    0.348 0.733304    
Railline                  1.599e-01    1.845e-02    8.667 5.33e-07 ***
Oilline                  -1.141e-01    1.664e-02   -6.860 7.81e-06 ***
Intlborder                1.595e-01    5.694e-02    2.801 0.014154 *  
Shoreline                -1.859e-02    2.201e-02   -0.844 0.412617    
pri_mayor                 6.439e-04    9.950e-03    0.065 0.949314    
state_misalign            4.601e-02    2.427e-02    1.896 0.078849 .  
Bin:Year                        NaN    0.000e+00      NaN      NaN    
Bin:StateBaja California -2.420e-01    1.806e-03 -134.038  < 2e-16 ***
Bin:StateChiapas          1.425e-01    4.123e-03   34.563 5.89e-15 ***
Bin:StateChihuahua        1.336e-01    2.753e-03   48.516  < 2e-16 ***
Bin:StateDurango          1.157e-01    1.214e-02    9.530 1.69e-07 ***
Bin:StateNayarit          7.869e-02    1.479e-02    5.322 0.000108 ***
Bin:StateOaxaca           1.478e-01    1.198e-03  123.355  < 2e-16 ***
Bin:StatePuebla           1.571e-01    4.133e-03   38.012 1.58e-15 ***
Bin:StateQuintana Roo     1.666e-01    1.290e-02   12.921 3.60e-09 ***
Bin:StateSinaloa          1.374e-01    4.765e-03   28.834 7.21e-14 ***
Bin:StateTamaulipas       7.997e-02    7.285e-03   10.978 2.91e-08 ***
Bin:StateTlaxcala         1.267e-01    1.224e-02   10.350 6.09e-08 ***
Bin:StateVeracruz         1.515e-01    1.566e-02    9.673 1.41e-07 ***
Bin:StateYucatan          1.576e-01    2.708e-03   58.185  < 2e-16 ***
Bin:StateZacatecas        1.545e-01    9.668e-03   15.982 2.19e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2012 on 1987 degrees of freedom
  (194 observations deleted due to missingness)
Multiple R-squared(full model): 0.1697   Adjusted R-squared: 0.1517 
Multiple R-squared(proj model): 0.07236   Adjusted R-squared: 0.05228 
F-statistic(full model, *iid*):9.445 on 43 and 1987 DF, p-value: < 2.2e-16 
F-statistic(proj model): 164.1 on 27 and 14 DF, p-value: 1.867e-13 



Call:
   felm(formula = if_audit ~ stag_ind_bin + Pres_misalign + population +      MajorHighway + MajorPort + Airports + Railline + Oilline +      Intlborder + Shoreline + pri_mayor + state_misalign + Bin:Year +      Bin:State | Bin + State | 0 | State, data = df_alt2) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.50367 -0.13730 -0.03652  0.05264  0.88578 

Coefficients:
                      Estimate Cluster s.e. t value Pr(>|t|)    
stag_ind_bin         7.911e-01    4.721e-02  16.755 4.30e-08 ***
Pres_misalign        1.204e-01    1.367e-01   0.881  0.40105    
population           2.087e-06    8.995e-07   2.320  0.04548 *  
MajorHighway         4.546e-02    5.290e-02   0.859  0.41247    
MajorPort                  NaN    0.000e+00     NaN      NaN    
Airports             2.885e-01    1.070e-01   2.697  0.02452 *  
Railline             4.413e-02    3.490e-02   1.265  0.23781    
Oilline             -1.838e-01    4.227e-02  -4.349  0.00185 ** 
Intlborder          -2.187e-02    2.561e-02  -0.854  0.41522    
Shoreline           -1.621e-01    9.105e-02  -1.780  0.10872    
pri_mayor            8.646e-02    5.311e-02   1.628  0.13798    
state_misalign      -9.206e-02    6.585e-02  -1.398  0.19557    
Bin:Year                   NaN    0.000e+00     NaN      NaN    
Bin:StateChiapas     7.045e-02    1.548e-02   4.552  0.00138 ** 
Bin:StateChihuahua   1.035e-01    9.320e-03  11.108 1.48e-06 ***
Bin:StateDurango     1.605e-01    2.107e-02   7.620 3.26e-05 ***
Bin:StateNayarit     2.971e-01    2.186e-02  13.591 2.65e-07 ***
Bin:StateOaxaca      4.455e-02    1.832e-03  24.323 1.61e-09 ***
Bin:StatePuebla            NaN    0.000e+00     NaN      NaN    
Bin:StateSinaloa     1.046e-01    1.554e-02   6.730 8.56e-05 ***
Bin:StateTamaulipas  1.464e-01    2.215e-02   6.612 9.80e-05 ***
Bin:StateZacatecas   1.112e-01    4.831e-03  23.018 2.62e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.283 on 170 degrees of freedom
  (12 observations deleted due to missingness)
Multiple R-squared(full model): 0.3787   Adjusted R-squared: 0.2654 
Multiple R-squared(proj model): 0.2881   Adjusted R-squared: 0.1583 
F-statistic(full model, *iid*):3.342 on 31 and 170 DF, p-value: 2.81e-07 
F-statistic(proj model):  1642 on 22 and 9 DF, p-value: 1.07e-13 


\begin{table}
\centering
\begin{talltblr}[         %% tabularray outer open
caption={Treatment Effect on Audit Selection},
note{}={Significance levels: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.},
]                     %% tabularray outer close
{                     %% tabularray inner open
colspec={Q[]Q[]Q[]},
column{2-3}={}{halign=c,},
column{1}={}{halign=l,},
hline{4}={1-3}{solid, black, 0.05em},
}                     %% tabularray inner close
\toprule
& Criminally-Entrenched Areas & Non-Entrenched Areas \\ \midrule %% TinyTableHeader
Mayoral Reelection & \num{0.791}*** & \num{0.006} \\
& (\num{0.047}) & (\num{0.055}) \\
Num.Obs. & \num{202} & \num{2031} \\
R2 & \num{0.379} & \num{0.170} \\
\bottomrule
\end{talltblr}
\end{table} 

Call:
   felm(formula = Audit_found ~ stag_ind_bin + Pres_misalign + population +      MajorHighway + MajorPort + Airports + Railline + Oilline +      Intlborder + Shoreline + pri_mayor + state_misalign | Bin +      State | 0 | State, data = df_alt4) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.40963 -0.05306  0.00869  0.05070  0.26368 

Coefficients:
                 Estimate Cluster s.e. t value Pr(>|t|)  
stag_ind_bin    2.204e-02    1.011e-01   0.218   0.8306  
Pres_misalign  -1.189e-02    8.983e-02  -0.132   0.8966  
population     -5.752e-08    7.508e-08  -0.766   0.4563  
MajorHighway   -4.541e-02    5.622e-02  -0.808   0.4327  
MajorPort       8.272e-03    6.705e-02   0.123   0.9036  
Airports        3.897e-02    3.311e-02   1.177   0.2588  
Railline        3.029e-02    4.473e-02   0.677   0.5094  
Oilline               NaN    0.000e+00     NaN      NaN  
Intlborder     -1.020e-01    5.557e-02  -1.836   0.0877 .
Shoreline       1.219e-02    5.061e-02   0.241   0.8132  
pri_mayor      -1.132e-01    8.979e-02  -1.261   0.2279  
state_misalign  2.019e-02    5.526e-02   0.365   0.7203  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1432 on 73 degrees of freedom
  (14 observations deleted due to missingness)
Multiple R-squared(full model): 0.4821   Adjusted R-squared: 0.2835 
Multiple R-squared(proj model): 0.1523   Adjusted R-squared: -0.1728 
F-statistic(full model, *iid*):2.427 on 28 and 73 DF, p-value: 0.001332 
F-statistic(proj model): 5.699 on 12 and 14 DF, p-value: 0.001456 



Call:
   felm(formula = Audit_found ~ stag_ind_bin + Pres_misalign + population +      MajorHighway + MajorPort + Airports + Railline + Oilline +      Intlborder + Shoreline + pri_mayor + state_misalign | Bin +      State | 0 | State, data = df_alt5) 

Residuals:
      Min        1Q    Median        3Q       Max 
-0.075949 -0.024374  0.000000  0.003421  0.143780 

Coefficients:
                 Estimate Cluster s.e. t value Pr(>|t|)    
stag_ind_bin   -7.469e-01    1.876e-01  -3.981 0.007271 ** 
Pres_misalign  -3.064e-01    5.067e-02  -6.047 0.000926 ***
population      2.992e-06    3.782e-07   7.913 0.000216 ***
MajorHighway   -3.072e-02    5.539e-02  -0.555 0.599247    
MajorPort             NaN    0.000e+00     NaN      NaN    
Airports        1.881e-02    7.889e-02   0.238 0.819524    
Railline        6.355e-01    1.730e-01   3.674 0.010407 *  
Oilline               NaN    0.000e+00     NaN      NaN    
Intlborder            NaN    0.000e+00     NaN      NaN    
Shoreline      -1.792e-01    9.574e-02  -1.872 0.110429    
pri_mayor      -4.734e-01    1.189e-01  -3.983 0.007259 ** 
state_misalign  3.549e-01    8.283e-02   4.284 0.005182 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.09207 on 6 degrees of freedom
  (2 observations deleted due to missingness)
Multiple R-squared(full model): 0.9031   Adjusted R-squared: 0.6123 
Multiple R-squared(proj model): 0.7356   Adjusted R-squared: -0.05765 
F-statistic(full model, *iid*):3.106 on 18 and 6 DF, p-value: 0.08311 
F-statistic(proj model): 73.03 on 12 and 6 DF, p-value: 1.716e-05 


\begin{table}
\centering
\begin{talltblr}[         %% tabularray outer open
caption={Treatment Effect on Audit Outcomes: Portion of Budget Correctly Allocated},
note{}={Significance levels: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.},
]                     %% tabularray outer close
{                     %% tabularray inner open
colspec={Q[]Q[]Q[]},
column{2-3}={}{halign=c,},
column{1}={}{halign=l,},
hline{4}={1-3}{solid, black, 0.05em},
}                     %% tabularray inner close
\toprule
& Criminally-Entrenched Areas & Non-Entrenched Areas \\ \midrule %% TinyTableHeader
Mayoral Reelection & \num{-0.747}** & \num{0.022} \\
& (\num{0.188}) & (\num{0.101}) \\
Num.Obs. & \num{25} & \num{102} \\
R2 & \num{0.903} & \num{0.482} \\
\bottomrule
\end{talltblr}
\end{table} 
[1] 1 2 3 4
Levels: 1 2 3 4
[1] 1 2 3 4
Levels: 1 2 3 4
# A tibble: 12 × 2
   stag_treat_bin   Bin
            <dbl> <dbl>
 1              2     1
 2              3     1
 3              0     1
 4              2     2
 5              3     2
 6              0     2
 7              3     3
 8              2     3
 9              0     3
10              2     4
11              3     4
12              0     4
# A tibble: 12 × 2
   stag_treat_bin   Bin
            <dbl> <dbl>
 1              2     1
 2              0     1
 3              3     1
 4              2     2
 5              3     2
 6              0     2
 7              2     3
 8              3     3
 9              0     3
10              2     4
11              3     4
12              0     4
[1] 1 2 3 4

   1    2    3    4 
1819 1696 1693 1693 
[1] 2 3 0

   0    2    3 
1384 4502 1015 
[1] -1 -2 1  0  2  3  4 
Levels: -2 -1 0 1 2 3 4
[1] -1 1  -2 0  2  3  4 
Levels: -2 -1 0 1 2 3 4
# A tibble: 3 × 2
  stag_treat_bin num_unique_states
           <dbl>             <int>
1              0                 4
2              2                26
3              3                 2
# A tibble: 3 × 2
  stag_treat_bin num_unique_states
           <dbl>             <int>
1              0                 1
2              2                18
3              3                 2
 [1] "(Intercept)"    "event_time::-2" "event_time::0"  "event_time::1" 
 [5] "event_time::2"  "event_time::3"  "event_time::4"  "Pres_misalign" 
 [9] "population"     "MajorHighway"   "MajorPort"      "Airports"      
[13] "Railline"       "Oilline"        "Intlborder"     "Shoreline"     
[17] "pri_mayor"      "state_misalign"
\begin{table}
\centering
\begin{talltblr}[         %% tabularray outer open
caption={Event Study: Impact on Attacks against Politicians},
note{}={Significance levels: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.},
]                     %% tabularray outer close
{                     %% tabularray inner open
colspec={Q[]Q[]Q[]},
column{2-3}={}{halign=c,},
column{1}={}{halign=l,},
hline{12}={1-3}{solid, black, 0.05em},
}                     %% tabularray inner close
\toprule
& Criminally-Entrenched Areas & Non-Entrenched Areas \\ \midrule %% TinyTableHeader
Event time = -2 & \num{-0.063} & \num{-0.016} \\
& (\num{0.121}) & (\num{0.015}) \\
Event time = 1 & \num{0.178}*** & \num{0.033}*** \\
& (\num{0.043}) & (\num{0.008}) \\
Event time = 2 & \num{0.020} & \num{0.008} \\
& (\num{0.043}) & (\num{0.009}) \\
Event time = 3 & \num{-0.064} & \num{0.092}*** \\
& (\num{0.192}) & (\num{0.013}) \\
Event time = 4 & \num{-0.056} & \num{0.033}* \\
& (\num{0.213}) & (\num{0.014}) \\
Num.Obs. & \num{880} & \num{6508} \\
R2 & \num{0.075} & \num{0.018} \\
R2 Adj. & \num{0.056} & \num{0.016} \\
AIC & \num{993.1} & \num{-1259.6} \\
BIC & \num{1079.2} & \num{-1137.5} \\
RMSE & \num{0.42} & \num{0.22} \\
Std.Errors & IID & IID \\
\bottomrule
\end{talltblr}
\end{table} 
[1] 231
[1] 1818
231 & 1818 [1] "(Intercept)"    "event_time::-2" "event_time::0"  "event_time::1" 
 [5] "event_time::2"  "event_time::3"  "event_time::4"  "Pres_misalign" 
 [9] "population"     "MajorHighway"   "MajorPort"      "Airports"      
[13] "Railline"       "Oilline"        "Intlborder"     "Shoreline"     
[17] "pri_mayor"      "state_misalign"
\begin{table}
\centering
\begin{talltblr}[         %% tabularray outer open
caption={Event Study: Impact on Attacks against Incumbents},
note{}={Significance levels: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.},
]                     %% tabularray outer close
{                     %% tabularray inner open
colspec={Q[]Q[]Q[]},
column{2-3}={}{halign=c,},
column{1}={}{halign=l,},
hline{12}={1-3}{solid, black, 0.05em},
}                     %% tabularray inner close
\toprule
& Criminally-Entrenched Areas & Non-Entrenched Areas \\ \midrule %% TinyTableHeader
Event time = -2 & \num{-0.025} & \num{-0.005} \\
& (\num{0.048}) & (\num{0.006}) \\
Event time = 1 & \num{0.024} & \num{-0.003} \\
& (\num{0.017}) & (\num{0.003}) \\
Event time = 2 & \num{-0.008} & \num{-0.004} \\
& (\num{0.017}) & (\num{0.003}) \\
Event time = 3 & \num{-0.027} & \num{0.003} \\
& (\num{0.076}) & (\num{0.005}) \\
Event time = 4 & \num{-0.022} & \num{-0.003} \\
& (\num{0.084}) & (\num{0.005}) \\
Num.Obs. & \num{880} & \num{6508} \\
R2 & \num{0.022} & \num{0.001} \\
R2 Adj. & \num{0.003} & \num{-0.001} \\
AIC & \num{-638.0} & \num{-13640.3} \\
BIC & \num{-552.0} & \num{-13518.2} \\
RMSE & \num{0.16} & \num{0.08} \\
Std.Errors & IID & IID \\
\bottomrule
\end{talltblr}
\end{table} 
[1] 231
[1] 1818
231 & 1818 [1] "(Intercept)"    "event_time::-2" "event_time::0"  "event_time::1" 
 [5] "event_time::2"  "event_time::3"  "event_time::4"  "Pres_misalign" 
 [9] "population"     "MajorHighway"   "MajorPort"      "Airports"      
[13] "Railline"       "Oilline"        "Intlborder"     "Shoreline"     
[17] "pri_mayor"      "state_misalign"
\begin{table}
\centering
\begin{talltblr}[         %% tabularray outer open
caption={Event Study: Impact on Attacks against Challengers},
note{}={Significance levels: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.},
]                     %% tabularray outer close
{                     %% tabularray inner open
colspec={Q[]Q[]Q[]},
column{2-3}={}{halign=c,},
column{1}={}{halign=l,},
hline{12}={1-3}{solid, black, 0.05em},
}                     %% tabularray inner close
\toprule
& Criminally-Entrenched Areas & Non-Entrenched Areas \\ \midrule %% TinyTableHeader
Event time = -2 & \num{-0.038} & \num{-0.011} \\
& (\num{0.108}) & (\num{0.013}) \\
Event time = 1 & \num{0.154}*** & \num{0.036}*** \\
& (\num{0.038}) & (\num{0.007}) \\
Event time = 2 & \num{0.028} & \num{0.012} \\
& (\num{0.038}) & (\num{0.008}) \\
Event time = 3 & \num{-0.037} & \num{0.089}*** \\
& (\num{0.170}) & (\num{0.012}) \\
Event time = 4 & \num{-0.034} & \num{0.036}** \\
& (\num{0.189}) & (\num{0.012}) \\
Num.Obs. & \num{880} & \num{6508} \\
R2 & \num{0.070} & \num{0.021} \\
R2 Adj. & \num{0.052} & \num{0.019} \\
AIC & \num{785.7} & \num{-2578.8} \\
BIC & \num{871.7} & \num{-2456.8} \\
RMSE & \num{0.37} & \num{0.20} \\
Std.Errors & IID & IID \\
\bottomrule
\end{talltblr}
\end{table} 
[1] 231
[1] 1818
231 & 1818[1] 0.0726464
[1] 0
 [1] "Bin"                           "Year"                         
 [3] "ADM1_PCODE"                    "ADM2_PCODE"                   
 [5] "State"                         "Municipality"                 
 [7] "Bin_attacks2"                  "Bin_inc"                      
 [9] "Bin_chal"                      "MajorHighway"                 
[11] "MajorPort"                     "Airports"                     
[13] "Railline"                      "Oilline"                      
[15] "Intlborder"                    "Shoreline"                    
[17] "Poppies"                       "MajCity"                      
[19] "MayorParty"                    "Incumbent"                    
[21] "population"                    "hom_rate"                     
[23] "Aguacate_sembrada_tonelada"    "Aguacate_valor_miles_de_pesos"
[25] "Limon_sembrada"                "Limon_valor_prod"             
[27] "id_number"                     "total_alt"                    
[29] "Governor_party"                "Pres_party"                   
[31] "state_misalign"                "Pres_misalign"                
[33] "Upper_misalign"                "inflorgcrime"                 
[35] "inflfinanciamiento"            "orgcrimpart"                  
[37] "orgcrimdecvoter"               "val_index"                    
[39] "pri_mayor"                     "treat_stag"                   
[41] "nCarteles_2010"                "Treated_neighbor"             
[43] "neighborhood_weights"          "Treat_neigh_yr"               
[45] "max_yr_v"                      "rel_TN_periods"               
[47] "if_treated"                    "extorsiones"                  
[49] "secuestrados"                  "robo_de_negocio"              
[51] "violaciones"                   "stag_treat_bin"               
[53] "party_alt"                     "mayor_party"                  
[55] "race.after"                    "margin"                       
[57] "ran_reel"                      "won_reel"                     
[59] "mg_inc"                        "stag_ind_bin"                 
[61] "ExtorsiÃ³n"                    "Feminicidio"                  
[63] "Homicidio.culposo"             "Homicidio.doloso"             
[65] "Lesiones.dolosas"              "Narcomenudeo"                 
[67] "Robo.a.casa.habitaciÃ³n"       "Robo.a.negocio"               
[69] "Robo.a.transeÃºnte.total"      "Robo.con.violencia"           
[71] "Robo.de.vehÃ­culo"             "Robo.en.transporte.pÃºblico"  
[73] "Secuestro"                     "Trata.de.personas"            
[75] "ViolaciÃ³n"                    "Violencia.familiar"           
[77] "ran"                           "won"                          
[79] "mg2"                           "Audit_found"                  
[81] "if_audit"                     
\begin{table}
\centering
\begin{talltblr}[         %% tabularray outer open
caption={Treatment Effect on Crimes (Poppies)},
note{}={Significance levels: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.},
]                     %% tabularray outer close
{                     %% tabularray inner open
colspec={Q[]Q[]Q[]Q[]Q[]},
column{2-5}={}{halign=c,},
column{1}={}{halign=l,},
hline{4}={1-5}{solid, black, 0.05em},
}                     %% tabularray inner close
\toprule
& Secuestro & Extorsion & Homicidio.doloso & Robo.a.negocio \\ \midrule %% TinyTableHeader
Mayoral Reelection & \num{0.027} & \num{-0.220} & \num{9.261}*** & \num{1.555} \\
& (\num{0.136}) & (\num{0.374}) & (\num{1.897}) & (\num{1.160}) \\
Num.Obs. & \num{94} & \num{94} & \num{94} & \num{94} \\
R2 & \num{0.088} & \num{0.277} & \num{0.346} & \num{0.761} \\
\bottomrule
\end{talltblr}
\end{table} 
\begin{table}
\centering
\begin{talltblr}[         %% tabularray outer open
caption={Treatment Effect on Crimes (no poppies)},
note{}={Significance levels: + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.},
]                     %% tabularray outer close
{                     %% tabularray inner open
colspec={Q[]Q[]Q[]Q[]Q[]},
column{2-5}={}{halign=c,},
column{1}={}{halign=l,},
hline{4}={1-5}{solid, black, 0.05em},
}                     %% tabularray inner close
\toprule
& Secuestro & Extorsion & Homicidio.doloso & Robo.a.negocio \\ \midrule %% TinyTableHeader
Mayoral Reelection & \num{-0.386}* & \num{-0.011} & \num{0.117} & \num{0.992} \\
& (\num{0.168}) & (\num{0.109}) & (\num{0.490}) & (\num{0.766}) \\
Num.Obs. & \num{1055} & \num{1055} & \num{1055} & \num{1055} \\
R2 & \num{0.132} & \num{0.425} & \num{0.233} & \num{0.445} \\
\bottomrule
\end{talltblr}
\end{table} 
 [1] "Bin"                           "Year"                         
 [3] "ADM1_PCODE"                    "ADM2_PCODE"                   
 [5] "State"                         "Municipality"                 
 [7] "Bin_attacks2"                  "Bin_inc"                      
 [9] "Bin_chal"                      "MajorHighway"                 
[11] "MajorPort"                     "Airports"                     
[13] "Railline"                      "Oilline"                      
[15] "Intlborder"                    "Shoreline"                    
[17] "Poppies"                       "MajCity"                      
[19] "MayorParty"                    "Incumbent"                    
[21] "population"                    "hom_rate"                     
[23] "Aguacate_sembrada_tonelada"    "Aguacate_valor_miles_de_pesos"
[25] "Limon_sembrada"                "Limon_valor_prod"             
[27] "id_number"                     "total_alt"                    
[29] "Governor_party"                "Pres_party"                   
[31] "state_misalign"                "Pres_misalign"                
[33] "Upper_misalign"                "inflorgcrime"                 
[35] "inflfinanciamiento"            "orgcrimpart"                  
[37] "orgcrimdecvoter"               "val_index"                    
[39] "pri_mayor"                     "treat_stag"                   
[41] "nCarteles_2010"                "Treated_neighbor"             
[43] "neighborhood_weights"          "Treat_neigh_yr"               
[45] "max_yr_v"                      "rel_TN_periods"               
[47] "if_treated"                    "extorsiones"                  
[49] "secuestrados"                  "robo_de_negocio"              
[51] "violaciones"                   "stag_treat_bin"               
[53] "party_alt"                     "mayor_party"                  
[55] "race.after"                    "margin"                       
[57] "ran_reel"                      "won_reel"                     
[59] "mg_inc"                        "stag_ind_bin"                 
[61] "ExtorsiÃ³n"                    "Feminicidio"                  
[63] "Homicidio.culposo"             "Homicidio.doloso"             
[65] "Lesiones.dolosas"              "Narcomenudeo"                 
[67] "Robo.a.casa.habitaciÃ³n"       "Robo.a.negocio"               
[69] "Robo.a.transeÃºnte.total"      "Robo.con.violencia"           
[71] "Robo.de.vehÃ­culo"             "Robo.en.transporte.pÃºblico"  
[73] "Secuestro"                     "Trata.de.personas"            
[75] "ViolaciÃ³n"                    "Violencia.familiar"           
[77] "ran"                           "won"                          
[79] "mg2"                           "Audit_found"                  
[81] "if_audit"                     
Twoways effects Within Model

Call:
plm(formula = stag_treat_bin ~ total_alt + Upper_misalign + state_misalign + 
    Pres_misalign + MajorHighway + MajorPort + Airports + Railline + 
    Oilline + Intlborder + Shoreline + MajCity + population + 
    pri_mayor + Poppies + Bin_attacks2, data = state_panel, effect = "twoways", 
    model = "within", index = c("ADM1_PCODE", "Year"))

Unbalanced Panel: n = 31, T = 3-4, N = 120

Residuals:
       Min.     1st Qu.      Median     3rd Qu.        Max. 
-6.1920e-03 -2.1537e-04  6.6042e-06  2.2472e-04  2.6440e-03 

Coefficients:
                  Estimate  Std. Error t-value Pr(>|t|)   
total_alt      -3.0593e-03  1.9570e-03 -1.5632 0.122511   
Upper_misalign  2.7618e-04  2.5246e-04  1.0939 0.277741   
state_misalign -1.5591e-04  7.3341e-04 -0.2126 0.832267   
Pres_misalign  -8.2370e-04  1.1141e-03 -0.7393 0.462190   
MajorHighway    5.1637e-02  1.7784e-02  2.9035 0.004933 **
MajorPort      -5.8226e-02  1.1705e-01 -0.4974 0.620443   
Airports        4.3549e-03  1.2410e-02  0.3509 0.726696   
Railline        1.3991e-02  1.4056e-02  0.9954 0.322986   
Oilline         1.7728e-02  1.8601e-02  0.9531 0.343820   
Intlborder      1.0134e-02  1.5784e-02  0.6420 0.522948   
Shoreline      -4.1462e-03  1.8845e-02 -0.2200 0.826505   
MajCity        -2.8484e-02  1.5654e-02 -1.8196 0.073097 . 
population      1.0177e-08  2.2914e-08  0.4442 0.658297   
pri_mayor       1.8002e-04  6.9731e-04  0.2582 0.797039   
Poppies         2.4961e-03  1.3384e-02  0.1865 0.852595   
Bin_attacks2    3.1449e-04  1.8227e-03  0.1725 0.863510   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Total Sum of Squares:    7.7821e-05
Residual Sum of Squares: 6.4166e-05
R-Squared:      0.17547
Adj. R-Squared: -0.40169
F-statistic: 0.931081 on 16 and 70 DF, p-value: 0.53868
\begin{table}
\centering
\begin{talltblr}[         %% tabularray outer open
caption={Logistic Regression of Covariates on Assignment of Treatment},
note{}={Significance levels: . p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.},
]                     %% tabularray outer close
{                     %% tabularray inner open
colspec={Q[]Q[]},
column{2}={}{halign=c,},
column{1}={}{halign=l,},
hline{28}={1-2}{solid, black, 0.05em},
}                     %% tabularray inner close
\toprule
& (1) \\ \midrule %% TinyTableHeader
Municipal partisan alternation & \num{-0.003} \\
& (\num{0.002}) \\
State-federal partisan misalignment & \num{0.000} \\
& (\num{0.000}) \\
State-Municipal misalignment & \num{-0.000} \\
& (\num{0.001}) \\
Federal-Municipal misalignment & \num{-0.001} \\
& (\num{0.001}) \\
Major highway & \num{0.052}** \\
& (\num{0.018}) \\
Major Port & \num{-0.058} \\
& (\num{0.117}) \\
Rail Line & \num{0.014} \\
& (\num{0.014}) \\
Oil Line & \num{0.018} \\
& (\num{0.019}) \\
International Border & \num{0.010} \\
& (\num{0.016}) \\
Population & \num{0.000} \\
& (\num{0.000}) \\
PRI mayor & \num{0.000} \\
& (\num{0.001}) \\
Poppies & \num{0.002} \\
& (\num{0.013}) \\
Attack Count & \num{0.000} \\
& (\num{0.002}) \\
Num.Obs. & \num{120} \\
R2 & \num{0.175} \\
\bottomrule
\end{talltblr}
\end{table} 

Call:
   felm(formula = if_audit ~ Poppies + Pres_misalign + population +      MajorHighway + MajorPort + Airports + Railline + Oilline +      Intlborder + Shoreline + pri_mayor + state_misalign | State +      Bin | 0 | State, data = panel) 

Residuals:
     Min       1Q   Median       3Q      Max 
-0.78006 -0.10130 -0.03413  0.00498  1.02126 

Coefficients:
                 Estimate Cluster s.e. t value Pr(>|t|)    
Poppies         3.525e-02    1.418e-02   2.486   0.0187 *  
Pres_misalign  -1.037e-02    2.456e-02  -0.422   0.6758    
population      2.621e-07    4.332e-08   6.050 1.21e-06 ***
MajorHighway    1.351e-02    9.687e-03   1.395   0.1733    
MajorPort       1.429e-01    9.074e-02   1.574   0.1259    
Airports        4.719e-02    1.872e-02   2.521   0.0173 *  
Railline        7.915e-02    3.201e-02   2.473   0.0193 *  
Oilline        -6.846e-02    2.887e-02  -2.371   0.0243 *  
Intlborder      5.570e-03    3.421e-02   0.163   0.8718    
Shoreline       3.674e-02    3.368e-02   1.091   0.2840    
pri_mayor       1.848e-02    9.686e-03   1.908   0.0660 .  
state_misalign  9.449e-03    1.359e-02   0.695   0.4922    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2452 on 7342 degrees of freedom
  (419 observations deleted due to missingness)
Multiple R-squared(full model): 0.1441   Adjusted R-squared: 0.1389 
Multiple R-squared(proj model): 0.0447   Adjusted R-squared: 0.03884 
F-statistic(full model, *iid*):27.48 on 45 and 7342 DF, p-value: < 2.2e-16 
F-statistic(proj model): 8.462 on 12 and 30 DF, p-value: 1.097e-06 


\begin{table}
\centering
\begin{talltblr}[         %% tabularray outer open
caption={Municipal Characteristics of Audit Selection},
note{}={Significance levels: . p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001.},
]                     %% tabularray outer close
{                     %% tabularray inner open
colspec={Q[]Q[]},
column{2}={}{halign=c,},
column{1}={}{halign=l,},
hline{24}={1-2}{solid, black, 0.05em},
}                     %% tabularray inner close
\toprule
& (1) \\ \midrule %% TinyTableHeader
Poppies & \num{0.035}* \\
& (\num{0.014}) \\
Federal partisan misalignment & \num{-0.010} \\
& (\num{0.025}) \\
State partisan misalignment & \num{0.009} \\
& (\num{0.014}) \\
Major highway & \num{0.014} \\
& (\num{0.010}) \\
Major Port & \num{0.143} \\
& (\num{0.091}) \\
Rail Line & \num{0.079}* \\
& (\num{0.032}) \\
Oil Line & \num{-0.068}* \\
& (\num{0.029}) \\
International Border & \num{0.006} \\
& (\num{0.034}) \\
Shoreline & \num{0.037} \\
& (\num{0.034}) \\
Population & \num{0.000}*** \\
& (\num{0.000}) \\
PRI mayor & \num{0.018}+ \\
& (\num{0.010}) \\
Num.Obs. & \num{7388} \\
R2 & \num{0.144} \\
\bottomrule
\end{talltblr}
\end{table} 
Unique Obs. & 2011 \\
Reading layer `mex_admbnda_adm2_govmex_20210618' from data source 
  `C:\Users\adeew\OneDrive\Documents\Reelect_Ref\BJPS\replication\data\maps\mex_admbnda_govmex_20210618_SHP\mex_admbnda_adm2_govmex_20210618.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 2457 features and 14 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -118.3665 ymin: 14.53507 xmax: -86.71074 ymax: 32.71863
Geodetic CRS:  WGS 84
  Shape_Leng Shape_Area  ADM2_ES ADM2_PCODE ADM2_REF ADM2ALT1ES ADM2ALT2ES
1  1.1390098 0.02536948    Abalá    MX31001    Abala       <NA>       <NA>
2  1.4907537 0.06764511  Abasolo    MX05001     <NA>       <NA>       <NA>
3  2.2185821 0.05329148  Abasolo    MX11001     <NA>       <NA>       <NA>
4  0.2858428 0.00422426  Abasolo    MX19001     <NA>       <NA>       <NA>
5  2.6097991 0.16511164  Abasolo    MX28001     <NA>       <NA>       <NA>
6  0.6343173 0.01075031 Abejones    MX20001     <NA>       <NA>       <NA>
               ADM1_ES ADM1_PCODE ADM0_ES ADM0_PCODE       date    validOn
1              Yucatán       MX31  México         MX 2020-06-23 2021-06-18
2 Coahuila de Zaragoza       MX05  México         MX 2020-06-23 2021-06-18
3           Guanajuato       MX11  México         MX 2020-06-23 2021-06-18
4           Nuevo León       MX19  México         MX 2020-06-23 2021-06-18
5           Tamaulipas       MX28  México         MX 2020-06-23 2021-06-18
6               Oaxaca       MX20  México         MX 2020-06-23 2021-06-18
  validTo                       geometry
1    <NA> MULTIPOLYGON (((-89.58713 2...
2    <NA> MULTIPOLYGON (((-100.9423 2...
3    <NA> MULTIPOLYGON (((-101.5271 2...
4    <NA> MULTIPOLYGON (((-100.3713 2...
5    <NA> MULTIPOLYGON (((-98.01956 2...
6    <NA> MULTIPOLYGON (((-96.62002 1...
[1] "numeric"
Simple feature collection with 6 features and 94 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -102.5831 ymin: 21.62227 xmax: -101.8542 ymax: 22.30607
Geodetic CRS:  WGS 84
  ADM2_PCODE Shape_Leng Shape_Area        ADM2_ES ADM2_REF ADM2ALT1ES
1    MX01001   2.420713 0.10289965 Aguascalientes     <NA>       <NA>
2    MX01001   2.420713 0.10289965 Aguascalientes     <NA>       <NA>
3    MX01001   2.420713 0.10289965 Aguascalientes     <NA>       <NA>
4    MX01001   2.420713 0.10289965 Aguascalientes     <NA>       <NA>
5    MX01002   1.804404 0.04805653       Asientos     <NA>       <NA>
6    MX01002   1.804404 0.04805653       Asientos     <NA>       <NA>
  ADM2ALT2ES        ADM1_ES ADM1_PCODE.x ADM0_ES ADM0_PCODE       date
1       <NA> Aguascalientes         MX01  México         MX 2020-06-23
2       <NA> Aguascalientes         MX01  México         MX 2020-06-23
3       <NA> Aguascalientes         MX01  México         MX 2020-06-23
4       <NA> Aguascalientes         MX01  México         MX 2020-06-23
5       <NA> Aguascalientes         MX01  México         MX 2020-06-23
6       <NA> Aguascalientes         MX01  México         MX 2020-06-23
     validOn validTo Bin Year ADM1_PCODE.y          State   Municipality
1 2021-06-18    <NA>   4 2021         MX01 Aguascalientes Aguascalientes
2 2021-06-18    <NA>   2 2015         MX01 Aguascalientes Aguascalientes
3 2021-06-18    <NA>   3 2018         MX01 Aguascalientes Aguascalientes
4 2021-06-18    <NA>   1 2012         MX01 Aguascalientes Aguascalientes
5 2021-06-18    <NA>   3 2018         MX01 Aguascalientes       Asientos
6 2021-06-18    <NA>   2 2015         MX01 Aguascalientes       Asientos
  Bin_attacks2 Bin_inc Bin_chal MajorHighway MajorPort Airports Railline
1            0       0        0            1         0        1        1
2            0       0        0            1         0        1        1
3            0       0        0            1         0        1        1
4            0       0        0            1         0        1        1
5            0       0        0            1         0        0        1
6            0       0        0            1         0        0        1
  Oilline Intlborder Shoreline Poppies MajCity     MayorParty Incumbent
1       1          0         0       0       1            PAN         0
2       1          0         0       0       1            PRI         0
3       1          0         0       0       1            PAN         0
4       1          0         0       0       1            PAN         0
5       1          0         0       0       0 PRI/ALIANZA/PT         0
6       1          0         0       0       0        ALIANZA         0
  population hom_rate Aguacate_sembrada_tonelada Aguacate_valor_miles_de_pesos
1     933576 3.320565                          0                             0
2     933576 2.784990                          0                             0
3     933576 5.784210                          0                             0
4     933576 2.142300                          0                             0
5      49759 6.029060                          0                             0
6      49759 4.019373                          0                             0
  Limon_sembrada Limon_valor_prod id_number total_alt Governor_party Pres_party
1              0                0         1         1            PRI        MRN
2              0                0         1         1            PRI        PRI
3              0                0         1         0            PRI        MRN
4              0                0         1         1            PRI        PAN
5              0                0         4         0            PRI        MRN
6              0                0         4         1            PRI        PRI
  state_misalign Pres_misalign Upper_misalign inflorgcrime inflfinanciamiento
1              0             0              1    0.4285715          0.8333333
2              1             1              0    0.4285715          0.8333333
3              0             0              1    0.4285715          0.8333333
4              0             0              0    0.4285715          0.8333333
5              0             0              1    0.4285715          0.8333333
6              0             0              0    0.4285715          0.8333333
  orgcrimpart orgcrimdecvoter val_index pri_mayor treat_stag nCarteles_2010
1           0               0         4         0       2016              2
2           0               0         4         1       2016              2
3           0               0         4         0       2016              2
4           0               0         4         0       2016              2
5           0               0         3         1       2016              1
6           0               0         3         0       2016              1
  Treated_neighbor neighborhood_weights Treat_neigh_yr max_yr_v rel_TN_periods
1                1                    0           2015     2015              6
2                1                    0           2015     2015              0
3                1                    0           2015     2015              3
4                0                    0              0     2015             -2
5                1                    0           2015     2015              3
6                1                    0           2015     2015              0
  if_treated extorsiones secuestrados robo_de_negocio violaciones
1          0   0.5045460   0.01750000       10.893606   2.0927190
2          1   0.2440261   0.03333333        8.997679   0.7821348
3          0   0.8239038   0.06500000       15.075052   1.3910840
4          1   0.5740508   0.00000000       11.170353   0.9454954
5          0   0.8239038   0.06500000       15.075052   1.3910840
6          1   0.2440261   0.03333333        8.997679   0.7821348
  stag_treat_bin party_alt mayor_party         race.after margin ran_reel
1              2         0     pan-prd               2024     NA        0
2              2         0         pan          Reelected 0.0872        1
3              2         0         pan Term-limited-p-won 0.2779        0
4              2         1         pan Term-limited-p-won 0.0536        0
5              2         1      morena          Out-p-won 0.1255        0
6              2         0  pri-pt-pna         Out-p-lost 0.2248        0
  won_reel mg_inc stag_ind_bin ExtorsiÃ³n Feminicidio Homicidio.culposo
1        0 0.0000            1  0.5474691  0.09156461         0.5822104
2        1 0.0872            1         NA          NA                NA
3        0 0.0000            1  0.7503609  0.04014320         0.9039349
4        0 0.0000            0         NA          NA                NA
5        0 0.0000            1  0.2752718  0.11088491         1.5496432
6        0 0.0000            1         NA          NA                NA
  Homicidio.doloso Lesiones.dolosas Narcomenudeo Robo.a.casa.habitaciÃ³n
1        0.3995418         21.09390     9.652649                11.95566
2               NA               NA           NA                      NA
3        0.5035161         23.77612    11.411960                16.57431
4               NA               NA           NA                      NA
5        0.5527701         18.14299     5.888342                13.16974
6               NA               NA           NA                      NA
  Robo.a.negocio Robo.a.transeÃºnte.total Robo.con.violencia Robo.de.vehÃ­culo
1      12.022120                 11.43184           6.000210          8.809991
2             NA                       NA                 NA                NA
3      14.081259                 13.92851           7.823751         17.666612
4             NA                       NA                 NA                NA
5       5.228564                  2.37048           1.430428          3.145584
6             NA                       NA                 NA                NA
  Robo.en.transporte.pÃºblico  Secuestro Trata.de.personas ViolaciÃ³n
1                    1.095958 0.01277303        0.02976796  2.2047964
2                          NA         NA                NA         NA
3                    1.028193 0.06138148        0.01759924  1.7181911
4                          NA         NA                NA         NA
5                    0.000000 0.00000000        0.05461186  0.8804413
6                          NA         NA                NA         NA
  Violencia.familiar ran won    mg2 Audit_found if_audit
1          15.765011   0   0 0.0000          NA        0
2                 NA   0   0 0.0000       1.000        1
3          12.691151   1   1 0.0872       0.710        1
4                 NA   0   0 0.0000       0.712        1
5           8.428193   0   0 0.0000          NA        0
6                 NA   0   0 0.0000          NA        0
                        geometry
1 MULTIPOLYGON (((-102.0978 2...
2 MULTIPOLYGON (((-102.0978 2...
3 MULTIPOLYGON (((-102.0978 2...
4 MULTIPOLYGON (((-102.0978 2...
5 MULTIPOLYGON (((-101.9994 2...
6 MULTIPOLYGON (((-101.9994 2...
Reading layer `mex_admbnda_adm1_govmex_20210618' from data source 
  `C:\Users\adeew\OneDrive\Documents\Reelect_Ref\BJPS\replication\data\maps\mex_admbnda_govmex_20210618_SHP\mex_admbnda_adm1_govmex_20210618.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 32 features and 12 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -118.3665 ymin: 14.53507 xmax: -86.71074 ymax: 32.71863
Geodetic CRS:  WGS 84
Simple feature collection with 6 features and 12 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -118.3665 ymin: 14.53507 xmax: -89.12123 ymax: 32.71863
Geodetic CRS:  WGS 84
              ADM1_ES ADM1_PCODE ADM1_REF ADM1ALT1ES ADM1ALT2ES ADM0_ES
1      Aguascalientes       MX01     <NA>       <NA>       <NA>  México
2     Baja California       MX02     <NA>       <NA>       <NA>  México
3 Baja California Sur       MX03     <NA>       <NA>       <NA>  México
4            Campeche       MX04     <NA>       <NA>       <NA>  México
5             Chiapas       MX07     <NA>       <NA>       <NA>  México
6           Chihuahua       MX08     <NA>       <NA>       <NA>  México
  ADM0_PCODE       date    validOn validTo Shape_Leng Shape_Area
1         MX 2020-06-23 2021-06-18    <NA>   3.993483  0.4911515
2         MX 2020-06-23 2021-06-18    <NA>  29.414983  6.8513179
3         MX 2020-06-23 2021-06-18    <NA>  51.783353  6.4860202
4         MX 2020-06-23 2021-06-18    <NA>  27.833408  4.7578445
5         MX 2020-06-23 2021-06-18    <NA>  26.835058  6.1504883
6         MX 2020-06-23 2021-06-18    <NA>  30.076365 22.8735457
                        geometry
1 MULTIPOLYGON (((-102.2879 2...
2 MULTIPOLYGON (((-114.1288 2...
3 MULTIPOLYGON (((-109.9103 2...
4 MULTIPOLYGON (((-91.55007 1...
5 MULTIPOLYGON (((-92.77034 1...
6 MULTIPOLYGON (((-106.5579 3...
R version 4.5.1 (2025-06-13 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22621)

Matrix products: default
  LAPACK version 3.12.1

locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/Denver
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] lubridate_1.9.4     forcats_1.0.0       stringr_1.5.1      
 [4] tidyr_1.3.1         tibble_3.3.0        tidyverse_2.0.0    
 [7] stringi_1.8.7       foreign_0.8-90      gridExtra_2.3      
[10] lmtest_0.9-40       zoo_1.8-14          sjPlot_2.9.0       
[13] MASS_7.3-65         glue_1.8.0          didimputation_0.3.0
[16] data.table_1.17.8   did2s_1.0.2         lfe_3.1.1          
[19] Matrix_1.7-3        purrr_1.1.0         broom_1.0.9        
[22] xtable_1.8-4        spdep_1.3-13        sf_1.0-21          
[25] spData_2.3.4        bacondecomp_0.1.1   readr_2.1.5        
[28] did_2.1.2           ggthemes_5.1.0      modelsummary_2.4.0 
[31] ggplot2_3.5.2       stargazer_5.2.3     kableExtra_1.4.0   
[34] fixest_0.12.1       plm_2.6-6           dplyr_1.1.4        
[37] tmap_4.1           

loaded via a namespace (and not attached):
  [1] splines_4.5.1           tinytable_0.11.0        leaflegend_1.2.1       
  [4] datawizard_1.2.0        hardhat_1.4.1           pROC_1.19.0.1          
  [7] XML_3.99-0.18           rpart_4.1.24            lifecycle_1.0.4        
 [10] Rdpack_2.6.4            rstatix_0.7.2           globals_0.18.0         
 [13] lattice_0.22-7          vroom_1.6.5             insight_1.3.1          
 [16] crosstalk_1.2.1         backports_1.5.0         magrittr_2.0.3         
 [19] rmarkdown_2.29          collapse_2.1.2          sp_2.2-0               
 [22] DBI_1.2.3               RColorBrewer_1.1-3      abind_1.4-8            
 [25] nnet_7.3-20             sandwich_3.1-1          ipred_0.9-15           
 [28] lava_1.8.1              dreamerr_1.5.0          listenv_0.9.1          
 [31] terra_1.8-60            units_0.8-7             performance_0.15.0     
 [34] bigmemory_4.6.4         parallelly_1.45.1       svglite_2.2.1          
 [37] codetools_0.2-20        xml2_1.3.8              tidyselect_1.2.1       
 [40] raster_3.6-32           farver_2.1.2            effectsize_1.0.1       
 [43] stats4_4.5.1            base64enc_0.1-3         caret_7.0-1            
 [46] cols4all_0.8            e1071_1.7-16            Formula_1.2-5          
 [49] survival_3.8-3          iterators_1.0.14        systemfonts_1.2.3      
 [52] foreach_1.5.2           tools_4.5.1             miscTools_0.6-28       
 [55] Rcpp_1.1.0              prodlim_2025.04.28      xfun_0.52              
 [58] leaflet.providers_2.0.0 withr_3.0.2             numDeriv_2016.8-1.1    
 [61] fastmap_1.2.0           boot_1.3-31             digest_0.6.37          
 [64] timechange_0.3.0        R6_2.6.1                textshaping_1.0.1      
 [67] colorspace_2.1-1        wk_0.9.4                spacesXYZ_1.6-0        
 [70] utf8_1.2.6              generics_0.1.4          recipes_1.3.1          
 [73] class_7.3-23            htmlwidgets_1.6.4       parameters_0.27.0      
 [76] tmaptools_3.3           ModelMetrics_1.2.2.2    pkgconfig_2.0.3        
 [79] gtable_0.3.6            timeDate_4041.110       fastglm_0.0.3          
 [82] htmltools_0.5.8.1       carData_3.0-5           scales_1.4.0           
 [85] BMisc_1.4.8             png_0.1-8               gower_1.0.2            
 [88] bigmemory.sri_0.1.8     knitr_1.50              rstudioapi_0.17.1      
 [91] uuid_1.2-1              tzdb_0.5.0              reshape2_1.4.4         
 [94] checkmate_2.3.2         nlme_3.1-168            bdsmatrix_1.3-7        
 [97] proxy_0.4-27            KernSmooth_2.23-26      parallel_4.5.1         
[100] s2_1.1.9                leafsync_0.1.0          pillar_1.11.0          
[103] grid_4.5.1              stringmagic_1.2.0       logger_0.4.0           
[106] vctrs_0.6.5             ggpubr_0.6.1            DRDID_1.2.2            
[109] car_3.1-3               maxLik_1.5-2.1          evaluate_1.0.4         
[112] cli_3.6.5               compiler_4.5.1          rlang_1.1.6            
[115] crayon_1.5.3            maptiles_0.10.0         future.apply_1.20.0    
[118] ggsignif_0.6.4          labeling_0.4.3          classInt_0.4-11        
[121] plyr_1.8.9              viridisLite_0.4.2       deldir_2.0-4           
[124] stars_0.6-8             tables_0.9.31           leaflet_2.2.2          
[127] bayestestR_0.16.1       hms_1.1.3               bit64_4.6.0-1          
[130] leafem_0.2.4            future_1.67.0           trust_0.1-8            
[133] rbibutils_2.3           lwgeom_0.2-14           bit_4.6.0              
